Hello everyone,
I would like to know if there are any plans to implement Parallel execution for canisters ?
Specially for hosting Liquid AI models on chain (as opposed to TEE) …e.g., parallel processing within a single canister.
Look forward to hearing from you
Kurt
Hey Kurt,
Yes, parallel execution is probably the main way to speed up computations these days. The big downside, though, is non-determinism: you might easily get slightly different results across replicas.
Wasmtime (the engine we’re using) has pretty good support for threads, but direct multi-threading in a canister isn’t on the roadmap. We’ve been looking into other options, @ielashi can give you more info.
Thanks for getting back to me on this …
Parallel execution’s the speed king, but non-determinism’s a pain=different results every time, ugh.
Wasmtime handles threads well, but canister multi-threading? Not happening. What about scheduling? Any smart tricks there to boost performance without the parallel mess? @ielashi
, thoughts?
Doesn’t multithreading within a canister defeat The Actor Model on which they are based?
Canisters as actors
Canisters are much like actors when thinking about them abstractly. Following the actor model of concurrent computation, canisters respond to messages they receive by performing one or more of the following actions:
- Modifying their private state.
- Sending messages to other canisters (actors).
- Creating more canisters (actors).
Although canisters have a single thread of execution, multiple can be executed concurrently. This is a key feature of ICP that overcomes the scaling challenges of other smart contract platforms.
No, single-threading is not a critical feature of an actor model. A multi-threaded canister can still modify its private state, send messages, and create new canisters. The execution mode is an implementation detail of an actor
I thought the whole idea of actors is that they’re supposed to be single threaded and that you get concurrency and parallelism via systems of actors? At least that was my impression a few years ago when I played around with the likes of Akka.NET.
Update: I’m wrong, see my follow-up.
Asked my Chinese friend and he came up with the following,
The AI Challenge on ICP
Training and inference require heavy computation while demanding:
AI-Specific Optimization Strategies
Code …
// Stable batch ordering using Blake3 hash
let batches = inputs.chunks(100).sorted_by_key(|b| blake3(b));
let results: Vec<Prediction> = batches
.map(|batch| model.predict(batch)) // Deterministic ops
.collect();
fixed crate for RustCode …
// Cross-canister gradient validation
let grad_a = canister_a.compute_gradients(batch).await;
let grad_b = canister_b.compute_gradients(batch).await;
assert_relative_eq!(grad_a, grad_b, epsilon = 1e-6);
ic-random (network-provided entropy)Compute local gradients (sorted input order)
Async aggregation with CRC32 checksums
Update model weights using deterministic optimizer steps
Hybrid Quantization
Balance speed vs precision
Code …
let quantized = quantize_layer(
weights,
scale: 0.0125, // Calibrated per layer
zero_point: 64
);
let output = dequantize(quantized, scale, zero_point);
Updates each run sequentially but queries can run in parallel.
So if you are able to make use of queries then you’re all sorted.
I did explore optimizing the execution of AI inference, and one optimization that would really make a difference would be support multi-core matrix multiplication, as that’s where 99% of all inference computation lies. Having said that, even with that optimization, don’t expect to be able to run models bigger than ~1B parameters efficiently, as for those we’d really need beefier hardware than the current nodes.
Computation aside, there’s also the I/O problem, so even with beefier hardware that allows us to run bigger models, swapping in/out these models to/from VRAM could be very expensive (a 70B parameter model would need anywhere between 10-20 seconds just to be loaded in VRAM). That’s another challenge that we’d still have even with beefier hardware.
That’s why for now we’re exploring the approach of AI workers as outlined here. With this approach, we’d support a handful of foundational LLMs rather than support anyone running any model. It’s a limitation, but it does allow these workers to have these LLMs consistently loaded in RAM for faster inference.
Are you interested specifically in training?
Hi Islam,
Thanks for the detailed reply—I really appreciate the insight into the current constraints and the AI workers approach! I’m working on an AI replica startup targeting the bereavement market, where privacy is paramount. My goal is to run Liquid AI models fully on-chain (max 8GB) on ICP, leveraging the security and control of a canister-based setup as our unique selling point. Inference is the priority, but some on-chain fine-tuning is also key to adapt models to individual users, with roughly 60-80% of each model’s structure being replicated across instances for consistency.
Your response got me thinking about how to make this work within ICP’s current framework, so I’ve got a few follow-up questions:
Model Swapping Optimization
For your pre-loaded foundational LLMs in the AI workers setup:
Are you exploring model partitioning (e.g., sharding layers across workers) to reduce per-node memory pressure? This could help fit an 8GB model efficiently.
Could quantized layers or adaptors (like LoRA) keep base models resident in RAM while swapping smaller, fine-tuned components? That might align with my need for lightweight personalization.
AI Workers Architecture
With this approach, would it support:
Custom fine-tuning atop foundational models via consensus-verified diffs? I’m picturing a way to tweak models on-chain without full retraining.
ZK proofs for inference integrity instead of replicating the entire model across nodes? This could cut overhead while keeping things secure and verifiable.
Training Horizon
Since some on-chain training is in scope for us, we’re curious about deterministic SGD possibilities:
Is there research into gradient averaging with fixed-point rounding guarantees? This could ensure reproducible results across canisters.
Any exploration of federated learning patterns using canister-to-device coordination? It might fit our privacy-first model if we can offload some computation to user devices.
I’d love to hear your thoughts on how feasible this is with ICP’s current trajectory—or if there’s a better angle to explore. Thanks again for your time!
Cheers,
Kurt
Yes, you are right. I misunderstood this. I asked You AI. See especially the answer to my follow-up question.
Good morning,
Your mention of the AI workers approach prompted me to explore parallel execution further. To that end, I’ve developed a conceptual Motoko implementation, inspired by PyPIM’s principles of deterministic, in-memory AI operations. Please see the code below , it focuses on parallel matrix multiplication via inter-canister parallelism and deterministic chunking. I believe its design, which distributes computation across child canisters and maintains data in memory, could be promising. Might this approach, splitting an 8GB model across canisters, operate effectively within the node constraints you outlined? Or would the I/O limitations still pose a significant challenge?
For your reference, here is the implementation I’ve created:
import Array “mo:base/Array”;
import Iter “mo:base/Iter”;
import Buffer “mo:base/Buffer”;
import Result “mo:base/Result”;
actor class ParentCanister() = this {
type Matrix = [[Float]];
type ChunkResult = { #ok : (Nat, Nat, [[Float]]); #err : Text };
// Step 1: Deterministic chunking
func chunkMatrix(matrix : Matrix, chunkSize : Nat) : [[Matrix]] {
let rows = matrix.size();
let cols = if rows > 0 then matrix[0].size() else 0;
Array.tabulate(
(rows + chunkSize - 1) / chunkSize,
func(i : Nat) : [Matrix] {
Array.tabulate(
(cols + chunkSize - 1) / chunkSize,
func(j : Nat) : Matrix {
let startRow = i * chunkSize;
let endRow = Nat.min(startRow + chunkSize, rows);
Array.tabulate(
endRow - startRow,
func(r : Nat) : [Float] {
let startCol = j * chunkSize;
let endCol = Nat.min(startCol + chunkSize, cols);
Array.tabulate(
endCol - startCol,
func(c : Nat) : Float { matrix[startRow + r][startCol + c] }
)
}
)
})
})
};
// Step 2: Distributed computation
public shared func distributedMatMul(
a : Matrix,
b : Matrix,
chunkSize : Nat,
children : [actor { computeChunk : (Matrix, Matrix) → async ChunkResult }]
) : async Result.Result<Matrix, Text> {
let aChunks = chunkMatrix(a, chunkSize);
let bChunks = chunkMatrix(b, chunkSize);
if (aChunks.size() != bChunks.size()) {
return #err("Matrix dimensions incompatible for multiplication");
};
let resultChunks = Buffer.Buffer<(Nat, Nat, [[Float]])>(aChunks.size() ** 2);
// Fan-out to child canisters
let futures = Buffer.Buffer<async ChunkResult>(aChunks.size());
for (i in Iter.range(0, aChunks.size()-1)) {
for (j in Iter.range(0, aChunks[i].size()-1)) {
let child = children[(i * aChunks.size() + j) % children.size()];
futures.add(
child.computeChunk(aChunks[i][j], bChunks[j][i]) // Transpose B
);
};
};
// Collect and validate results
let results = await Array.mapFilter<async ChunkResult, (Nat, Nat, [[Float]])>(
futures.toArray(),
func(f) = async {
switch (await f) {
case (#ok res) ?res;
case (#err _) null;
}
}
);
// Step 3: Deterministic aggregation
let sorted = Array.sort(
results,
func(a : (Nat, Nat, [[Float]]), b : (Nat, Nat, [[Float]])) : { #less; #equal; #greater } {
if (a.0 < b.0) #less
else if (a.0 > b.0) #greater
else if (a.1 < b.1) #less
else if (a.1 > b.1) #greater
else #equal
}
);
// Reconstruct final matrix
let totalRows = a.size();
let totalCols = if b.size() > 0 then b[0].size() else 0;
var result = Array.tabulate<[Float]>(totalRows, func(_) = Array.init<Float>(totalCols, 0.0));
for ((i, j, chunk) in sorted.vals()) {
for (row in Iter.range(0, chunk.size()-1)) {
for (col in Iter.range(0, chunk[row].size()-1)) {
let globalRow = i * chunkSize + row;
let globalCol = j * chunkSize + col;
result[globalRow][globalCol] := chunk[row][col];
};
};
};
#ok(result)
};
};
actor class ChildCanister() = this {
// Step 4: SIMD-style batch processing
public func computeChunk(a : [[Float]], b : [[Float]]) : async {
#ok : (Nat, Nat, [[Float]]);
#err : Text
} {
if (a.size() == 0 or b.size() == 0 or a[0].size() != b.size()) {
return #err(“Invalid chunk dimensions”);
};
let result = Array.tabulate<[Float]>(a.size(),
func(i : Nat) : [Float] {
Array.tabulate<Float>(b[0].size(),
func(j : Nat) : Float {
var sum = 0.0;
for (k in Iter.range(0, a[0].size()-1)) {
sum += a[i][k] * b[k][j];
};
sum
})
});
// Return with original chunk coordinates
#ok(0, 0, result) // Actual coordinates would need tracking
};
};
This implementation incorporates deterministic chunking, inter-canister parallelism, and a sorted aggregation process. For production use, I anticipate enhancements such as memory management and cycle cost accounting would be necessary.
Building on your previous insights, I have a few additional questions:
Model Swapping Optimisation
Regarding your AI workers, are you considering sharding model layers across nodes to reduce memory demands? This could align with my chunking strategy.
Would quantised layers or LoRA adaptors enable base models to remain resident while swapping smaller components? This might suit my fine-tuning requirements.
AI Workers Architecture
Could your approach accommodate fine-tuning through consensus-verified differences? My chunked updates share some similarities with this concept.
Is there potential to use zero-knowledge proofs for inference integrity rather than full model replication? This could enhance efficiency while maintaining security.
Training Horizon
Given my interest in on-chain training, has there been exploration of deterministic stochastic gradient descent with fixed-point rounding for gradient averaging? My aggregation method seeks to ensure such consistency.
Are there plans to investigate federated learning via canister-to-device coordination? This could support privacy while distributing computational load.
Sorry for all the questions I appreciate you are all busy.
Cheers
Kurt
Distributed DataFrame Processor,
import Array “mo:base/Array”;
import Buffer “mo:base/Buffer”;
import Result “mo:base/Result”;
import Float “mo:base/Float”;
import Nat “mo:base/Nat”;
import Nat8 “mo:base/Nat8”;
import Cycles “mo:base/ExperimentalCycles”;
import Time “mo:base/Time”;
import Blob “mo:base/Blob”;
import Text “mo:base/Text”;
import BTree “mo:base/BTree”;
import Iter “mo:base/Iter”;
import Hash “mo:base/Hash”;
import Error “mo:base/Error”;
actor class SmallPond() = this {
type Column = {
name : Text;
dtype : Text; // Added data type support
values : [Float]
};
type DataFrame = {
columns : [Column];
rowCount : Nat;
chunkIndex : Nat; // Track chunk position
};
type Metrics = {
executionTime : Int;
cyclesUsed : Nat;
memoryUsed : Nat;
chunksProcessed : Nat;
compressionRatio : Float; // New metric
};
// Stable storage
stable let CHUNK_SIZE = 1000; // Rows per chunk
stable let MAX_METRIC_ENTRIES = 100;
stable var dataStore = BTree.init<Text, Blob>(Text.compare);
stable var metricsLog = Buffer.Buffer(MAX_METRIC_ENTRIES);
let CYCLE_BUDGET_PER_CHUNK : Nat = 10_000_000_000; // 10 T cycles
// Improved serialization with header metadata
func serializeDataFrame(df : DataFrame) : Blob {
let header = Text.encodeUtf8(
“DFv1|” # Nat.toText(df.rowCount) # “|” # Nat.toText(df.chunkIndex) # “|”
);
let columnBuffer = Buffer.Buffer<Nat8>(1024);
for (col in df.columns.vals()) {
let colHeader = Text.encodeUtf8(
col.name # "|" # col.dtype # "|" # Nat.toText(col.values.size()) # "|"
);
columnBuffer.append(colHeader);
// Pack floats as 4-byte little-endian
for (v in col.values.vals()) {
let bytes = Float.toBytes(v);
columnBuffer.append(Blob.toArray(bytes));
}
};
Blob.fromArray(Array.append(Blob.toArray(header), columnBuffer.toArray()))
};
func deserializeDataFrame(blob : Blob) : Result.Result<DataFrame, Text> {
try {
let data = Blob.toArray(blob);
let (header, rest) = splitAt(data, 5);
if (Text.decodeUtf8(Blob.fromArray(header)) != ?“DFv1|”) {
return #err(“Invalid file format”);
};
// Parse header
let (rowCountStr, rest) = parseUntil(rest, '|');
let (chunkStr, rest) = parseUntil(rest, '|');
let rowCount = Nat.fromText(rowCountStr) else return #err("Invalid row count");
let chunkIndex = Nat.fromText(chunkStr) else return #err("Invalid chunk index");
var remaining = rest;
let columns = Buffer.Buffer<Column>(10);
while (remaining.size() > 0) {
let (name, rem1) = parseUntil(remaining, '|');
let (dtype, rem2) = parseUntil(rem1, '|');
let (valCountStr, rem3) = parseUntil(rem2, '|');
let valCount = Nat.fromText(valCountStr) else return #err("Invalid value count");
let floatBytes = 4 * valCount;
if (rem3.size() < floatBytes) return #err("Incomplete data");
let values = Buffer.Buffer<Float>(valCount);
for (i in Iter.range(0, valCount-1)) {
let start = i * 4;
let bytes = Array.subArray(rem3, start, 4);
values.add(Float.fromBytes(Blob.fromArray(bytes)));
};
columns.add({
name = Text.decodeUtf8(Blob.fromArray(name))
else return #err("Invalid column name");
dtype = Text.decodeUtf8(Blob.fromArray(dtype))
else return #err("Invalid dtype");
values = values.toArray();
});
remaining := Array.subArray(rem3, floatBytes, rem3.size() - floatBytes);
};
#ok({
columns = columns.toArray();
rowCount;
chunkIndex;
})
} catch e {
#err(Error.message(e))
}
};
// Chunk-aware write operation
public shared({ caller }) func writeParquet(
df : DataFrame,
fileKey : Text
) : async Result.Result<(), Text> {
let startTime = Time.now();
var totalCyclesUsed = 0;
let totalChunks = df.rowCount / CHUNK_SIZE + (if df.rowCount % CHUNK_SIZE > 0 then 1 else 0);
var cyclesBudget = CYCLE_BUDGET_PER_CHUNK * totalChunks;
Cycles.add(cyclesBudget);
try {
// Split into chunks
for (chunkIdx in Iter.range(0, totalChunks - 1)) {
let chunkStart = chunkIdx * CHUNK_SIZE;
let chunkEnd = Nat.min(chunkStart + CHUNK_SIZE, df.rowCount);
let chunk = {
columns = Array.map(df.columns, func (col) {
let values = Array.subArray(col.values, chunkStart, chunkEnd - chunkStart);
{ col with values }
});
rowCount = chunkEnd - chunkStart;
chunkIndex = chunkIdx;
};
// Check cycle budget
if (Cycles.available() < CYCLE_BUDGET_PER_CHUNK) {
throw Error.reject("Insufficient cycles for chunk processing");
};
let serialized = serializeDataFrame(chunk);
dataStore := BTree.insert(dataStore, Text.compare,
makeChunkKey(fileKey, chunkIdx), serialized);
totalCyclesUsed += (Cycles.available() - Cycles.balance());
Cycles.refund(Cycles.balance()); // Return unused cycles
};
logMetrics(startTime, totalCyclesUsed, totalChunks, "write");
#ok(())
} catch e {
#err(Error.message(e))
}
};
// Parallel read with chunk aggregation
public shared query func readParquet(
fileKey : Text
) : async Result.Result<DataFrame, Text> {
let startTime = Time.now();
let chunks = BTree.scan(dataStore, Text.compare,
func(k, v) {
if (Text.startsWith(k, fileKey)) ?v else null
});
let dfBuffer = Buffer.Buffer<DataFrame>(10);
var totalRows = 0;
for (chunk in chunks) {
switch (deserializeDataFrame(chunk)) {
case (#ok(df)) {
dfBuffer.add(df);
totalRows += df.rowCount;
};
case (#err(msg)) return #err("Invalid chunk: " # msg);
};
};
if (dfBuffer.size() == 0) return #err("File not found");
// Merge chunks
let first = dfBuffer.get(0);
let merged = {
columns = Array.map(first.columns, func (col) {
let values = Buffer.Buffer<Float>(totalRows);
for (df in dfBuffer.vals()) {
let found = Array.find(df.columns, func (c) = c.name == col.name);
switch (found) {
case (?c) values.append(c.values);
case null return { col with values = [] }; // Error case
};
};
{ col with values = values.toArray() }
});
rowCount = totalRows;
chunkIndex = 0;
};
logMetrics(startTime, 0, dfBuffer.size(), "read");
#ok(merged)
};
// Improved metrics with compression stats
func logMetrics(
startTime : Int,
cyclesUsed : Nat,
chunks : Nat,
operation : Text
) {
let entry = {
executionTime = Time.now() - startTime;
cyclesUsed;
memoryUsed = chunks * CHUNK_SIZE * 4; // Approx bytes
chunksProcessed = chunks;
compressionRatio = calculateCompressionRatio();
};
if (metricsLog.size() >= MAX_METRIC_ENTRIES) {
metricsLog.remove(0);
};
metricsLog.add(entry);
};
func calculateCompressionRatio() : Float {
// Implement actual compression ratio calculation
0.5 // Placeholder
};
// Helper functions
func makeChunkKey(base : Text, idx : Nat) : Text {
base # “_chunk” # Nat.toText(idx)
};
func parseUntil(data : [Nat8], term : Char) : (Text, [Nat8]) {
let end = Array.find(data, func (b) = Char.fromNat32(Nat8.toNat32(b)) == term);
switch (end) {
case (?idx) {
let (head, rest) = Array.split(data, idx);
(Text.decodeUtf8(Blob.fromArray(head)), Array.subArray(rest, 1, rest.size() - 1))
};
case null (Text.decodeUtf8(Blob.fromArray(data)), )
}
};
};
I see SIMD mentioned, maybe this is relevant: https://internetcomputer.org/docs/building-apps/network-features/simd
Given the breadth of the questions you ask, how about we have a call, learn about your use-case, and discuss the points you mentioned? Feel free to DM me.
How does JAM a new technology developed by polkadot fixing all our limitations ? This is something that a developer said.
So on ICP we can execute just 1B parameters, when chat gpt models are 1.8T parameters, is this a joke? And we doesn’t even have a solution yet? How are we gonna be the world computer and solving AI security issues with 1B parameters execution.
The real world use is about finally making decentralized computing competitive with centralized cloud solutions. Not just creating a decentralized cloud computer that can host regular things, but not actually running them at global adoption level speeds.
JAM provides drastically more computing power than any other blockchain today, making it able to actually compete with web2 options in terms of computing power and costs.
While it will cost more for now to use Polakdot. The added bonuses for many will vastly outweigh the costs. You’re paying for added security and verification.
Why don’t I just list some things again.
General Enterprise Adoption… Companies can now run complex computations in a decentralized way, without depending on AWS, Google, or Microsoft.
AI & Machine Learning. Training and running decentralized AI models on-chain, with verifiable, tamper-proof execution. (This cannot be done in other chains, without costing millions, and having ungodly requiring amount of time to train)
Decentralized Finance at Scale. No more gas limitations, allowing high-frequency trading, real-time risk analysis, and automated financial modeling directly on-chain.
Gaming & Metaverse… JAM can actually compute On-chain physics engines, complex game logic, and AI-driven NPCs. all running natively at near-hardware speeds. ( no other chain can even remotely come close to being able to run On-chain physics engines, complex game logic, and AI-driven NPCs all simultaneously for cheap enough, and at fast enough speeds)
Sorry to say this, but the “solution “ or
Those off chain agents are useless, and that the whole industry web2 and web3 are doing, so we enter in the phase where ICP isn’t at the forefront,
Instead we have fell many steps back.
I suggested a solution to @ielashi on Friday, but understandably he is busy.
Also was off topic/ thread so I’ll post it again in full.
PyPIM
Kurt
What We’re Upgrading To
PyPIM needs memristive crossbar hardware—think fancy memory chips that compute right where the data sits (Sec. III, PyPIM paper). Current ICP nodes don’t have this—they’re beefy servers with 64 CPU cores, 512GB RAM, and 30TB NVMe SSDs (per ICP node specs). To run PyPIM, you’d either:
Add memristive modules to existing nodes (like a GPU-style upgrade).
Replace the whole rig with a new design built around memristive tech.
No clue if ICP nodes have spare slots for this, so we’ll explore both options. Costs depend on hardware, installation, and whatever else these node providers have to deal with.
Option 1: Add Memristive Modules
What’s Needed:
Memristive crossbars (e.g., 1,024x1,024 arrays per Sec. III-D, PyPIM paper).
Interface hardware (controllers, wiring) to hook it into the current setup.
Maybe extra power or cooling—memristors are low-power, but still.
Cost Breakdown:
Memristive Chips: These aren’t mass-market yet. Research-grade memristor arrays (like from Knowm or Crossbar Inc.) can run $100-$1,000 per chip depending on size and yield. Let’s say each crossbar’s 1,024x1,024—call it $500 a pop. PyPIM sims use multiple crossbars (8GB total, Table III), so maybe 10-20 chips per node. That’s $5,000-$10,000 for the arrays.
Controllers: Think FPGA or ASIC to manage the crossbars—$500-$2,000 per node, depending on complexity. Say $1,000.
Installation: Labour, testing, downtime. Node providers pay data centre fees (OPEX), so tack on a few hours at $50-$100/hour—let’s call it $500.
Power/Cooling Upgrades: Memristors sip power (way less than GPUs), so maybe $200-$500 if you need a tweak.
Total Per Node:
Low end: $5,000 (chips) + $1,000 (controller) + $500 (install) + $200 (extras) = ~$6,700.
High end: $10,000 + $2,000 + $500 + $500 = ~$13,000.
Catch: This assumes the current ICP node (e.g., Dell PowerEdge 6525) can handle add-ons. If it’s maxed out on slots or power, you’re stuffed—this won’t fly.
Option 2: Full Node Replacement
What’s Needed:
A new server built from scratch with memristive tech as the core—CPUs, RAM, SSDs, plus the crossbars.
Same 8GB memristive capacity (PyPIM’s baseline) but scaled for ICP’s 512GB RAM and 30TB storage needs.
Cost Breakdown:
Current ICP Node Cost: Gen-1 nodes (e.g., PowerEdge 6525) are high-end—retail’s ~$20,000-$30,000 each (CPU, RAM, SSDs, etc.). Node providers might get bulk deals, say $15,000-$25,000.
Memristive Overhaul: Swap some RAM/CPU grunt for crossbars. Keep 30TB SSDs ($5,000-$7,000), drop to 256GB RAM ($2,000), smaller CPU (32 cores, $1,500), then add $5,000-$10,000 in crossbars and $1,000 controller. Base chassis and extras (power, cooling, networking) ~$5,000.
New Total: $5,000 (SSDs) + $2,000 (RAM) + $1,500 (CPU) + $7,500 (crossbars avg) + $1,000 (controller) + $5,000 (base) = ~$22,000-$27,000.
Delta: If a current node’s $20,000, the upgrade cost is the difference—$2,000-$7,000 extra per node.
Catch: This assumes memristive tech scales cheaply. If 8GB jumps to 512GB-equivalent compute capacity (PyPIM’s dream), crossbar costs could balloon—$50,000+ easy. No data on that yet.
Other Costs
Shipping: Nodes go to data centres worldwide. $500-$1,000 per node, depending on distance.
NNS Approval: Node providers submit proposals to ICP’s Network Nervous System—free, but time’s a factor.
OPEX: Data centre rent, power, bandwidth. Memristors might drop power costs (less heat than CPUs), but base OPEX is $1,000-$2,000/month per node—unchanged unless scaled up.
How Many Nodes?
ICP’s got hundreds of nodes across subnets (exact count’s fuzzy—DFinity’s tight-lipped, but think 300-500 based on subnet topology). Per node cost scales network-wide:
Option 1: $6,700-$13,000 × 400 nodes = $2.7M-$5.2M.
Option 2 Delta: $2,000-$7,000 × 400 = $0.8M-$2.8M.
Reality Check
Availability: Memristive hardware’s still niche—HP and Intel have prototypes, but mass production’s years off. Costs could drop (or spike) by 2025.
ICP Fit: PyPIM’s 8GB sim (Table III) is tiny vs. ICP’s 512GB RAM nodes. Scaling memristors to match might push costs way higher—$50,000-$100,000 if you need hundreds of crossbars.
Rewards: Node providers earn ~$1.5M/month network-wide in ICP tokens (pegged to USD/SDR). A $13,000 upgrade pays back quick if rewards hold—months, not years.
Final Guess
Per Node Cost:
Add-On: $6,700-$13,000 if it’s just modules. Lekker if it works.
Replacement Delta: $2,000-$7,000 on top of current cost—cheaper but riskier.
Total Network Cost: Millions, depending on scale and adoption.