Consensus is a solved problem. Modern L1s like Solana and Sui use optimized variants of Proof-of-Stake (Narwhal-Bullshark, Sealevel) that process transactions in parallel. The theoretical throughput ceiling is no longer a function of block time or validator count.
comparison-of-consensus-mechanisms
Blog
Why Memory Bandwidth Will Dictate the Next Generation of L1s
An analysis of how RAM access speed, not consensus algorithms, is becoming the fundamental constraint for parallel execution engines, shaping the architecture of Solana, Aptos, Sui, and Monad.
introduction
THE BOTTLENECK
Introduction: The Consensus Illusion
The next generation of high-performance L1s will be constrained not by consensus algorithms, but by the physical memory bandwidth of their nodes.
The real bottleneck is data movement. Parallel execution engines must load and store massive state data (account balances, AMM pools, NFT metadata) from RAM. The speed of this operation is governed by a node's memory bandwidth, a physical hardware limit.
High TPS is a memory bandwidth tax. A 100,000 TPS chain like Solana requires its RPC nodes to sustain memory bandwidth exceeding 200 GB/s. This creates centralization pressure, as only data centers with high-end hardware can run full nodes.
Evidence: The Solana network regularly hits 100 Gbps of network traffic, with state accounts consuming over 200 GB of RAM. This forces RPC providers like Helius and Triton to operate custom, high-memory-bandwidth servers, not commodity cloud instances.
key-insights
THE NEXT BOTTLENECK
Executive Summary: The Bandwidth Thesis
As L1s solve for compute and storage, the fundamental physical limit of memory bandwidth emerges as the ultimate constraint on performance and decentralization.
01
The Problem: The von Neumann Bottleneck
Modern blockchain VMs are memory-bound, not compute-bound. The CPU spends most of its time waiting for data from RAM, not processing it. This caps throughput regardless of core count.
- Key Limitation: Memory bandwidth scales ~5% annually vs. compute's ~20% (Moore's Law).
- Network Effect: High-bandwidth nodes centralize to expensive hardware, killing decentralization.
- Real Cost: This bottleneck manifests as high gas fees during congestion, not slow execution.
~5%
BW Growth/Yr
1000x
BW vs. Compute Gap
02
The Solution: Bandwidth-Optimized Architectures
Next-gen L1s like Monad and Sei v2 are designing state machines and execution engines from first principles to minimize data movement.
- Parallel Execution: Requires massive, low-latency access to state; only possible with optimized memory hierarchies.
- Pipelining: Separates execution, consensus, and mempool stages to keep hardware units saturated, not idle.
- State Access Patterns: Algorithms are redesigned for locality of reference, keeping hot data in fast caches (L1/L2/L3).
10,000+
TPS Target
~1s
Time to Finality
03
The Trade-Off: Decentralization at Scale
High bandwidth demands create a centralization force. The winning L1 will be the one that maximizes bandwidth efficiency for the commodity hardware frontier.
- Node Requirements: If an L1 requires >1 GB/s memory bandwidth, it excludes most consumer hardware and all consumer-grade VPS.
- The Benchmark: Sustainable decentralization likely caps at the performance tier of a high-end gaming PC or AWS c6i instance.
- Implication: L1s that chase theoretical peak TPS on specialized hardware will fail to achieve credible neutrality.
<$200/mo
Target Node Cost
10k+
Node Target
04
The Proof: Ethereum's Data-Sharding Pivot
Ethereum's roadmap is the canonical case study. It abandoned execution sharding for data-availability sharding (Danksharding) because moving data is the harder problem.
- Core Insight: Scaling execution is easy if you have cheap, abundant, and verifiable data bandwidth (via EIP-4844 blobs).
- Rollup-Centric Future: L2s like Arbitrum, Optimism, and zkSync become the execution engines; Ethereum L1 becomes a bandwidth-optimized data-availability and settlement layer.
- Validation: This architecture proves that optimizing the data layer is more critical than optimizing the execution layer for the base chain.
~$0.001
Target Blob Cost
100x
DA Capacity Inc.
05
The Competitor: Solana's Brute-Force Approach
Solana's thesis is that hardware advancement will outpace adoption, so optimizing for maximum bandwidth today is correct. It validates the bandwidth thesis by being the only chain to demand it.
- Hardware Requirements: Requires >100 Gbps network and high RAM bandwidth, leading to validator centralization in institutional data centers.
- Performance Ceiling: Its current ~5k TPS is not limited by consensus but by the bandwidth of its weakest required validator.
- Strategic Risk: Bets entirely on continued exponential bandwidth growth, which is slowing per industry data.
~5k
Sustained TPS
~2k
Active Validators
06
The Metric: Bandwidth per Dollar
Forget TPS. The key metric for evaluating L1 scalability is sustainable bandwidth per dollar of node operational cost. This measures economic decentralization at scale.
- Calculation: (State Read/Write Bandwidth) / (Monthly Node Cost).
- Benchmarking: Compare Monad's pipelined engine vs. Aptos' parallel execution vs. Sui's object model on this metric.
- VC Takeaway: Invest in teams that architect for this metric, not theoretical peak throughput. The winner maximizes this ratio for the broadest hardware set.
Key KPI
BW/$
Mass Adoption
Determinant
thesis-statement
THE BOTTLENECK
The Core Argument: Throughput = f(Bandwidth, Cores)
The next generation of high-performance L1s will be defined by memory bandwidth, not just parallel execution cores.
Blockchain execution is memory-bound. Modern parallel VMs like Solana's Sealevel and Sui's Move process thousands of transactions concurrently, but their speed is limited by how fast data moves between RAM and the CPU. Adding more cores without increasing bandwidth creates idle processors.
Bandwidth dictates real-world TPS. A chain's theoretical peak throughput is the product of its memory bandwidth and the average data footprint per transaction. This is why Aptos and Sui focus on efficient state access patterns and data structures to minimize this footprint.
The evidence is in hardware. High-performance validators now require server-grade CPUs (AMD EPYC, Intel Xeon) with multi-channel DDR5 memory, not just high core counts. The move to 1TB/s+ memory systems is the next frontier for chains targeting 100k+ TPS.
This redefines decentralization trade-offs. Bandwidth-optimized hardware is more specialized than commodity cloud instances, creating a centralizing force. The winning L1 architecture will optimize for this physical constraint while maintaining a viable validator set.
THE BOTTLENECK SHIFT
Hardware Realities: Latency & Bandwidth Hierarchy
This table compares the primary hardware constraints for different blockchain node architectures, illustrating why memory bandwidth is becoming the critical bottleneck for high-throughput L1s like Solana and Monad.
| Hardware Metric / Constraint | Traditional EVM L1 (e.g., Ethereum) | High-Throughput L1 (e.g., Solana) | Next-Gen Parallel EVM (e.g., Monad) |
|---|---|---|---|
Primary Bottleneck | Network & Consensus Latency | CPU Execution | Memory Bandwidth (DRAM) |
Target Block Time | 12 seconds | 400 milliseconds | 1 second |
Peak Memory Bandwidth Demand | ~50 GB/s | ~200 GB/s |
|
Typical Node Hardware | Consumer CPU, 32 GB RAM | High-clock CPU, 128+ GB RAM | Server CPU (EPYC/Xeon), 256+ GB RAM |
State Access Pattern | Sparse, Sequential | Dense, Semi-Parallel | Dense, Massively Parallel |
Requires NVMe/SSD Optimized State DB | |||
Requires High-Bandwidth Memory (HBM) for VMs | |||
Theoretical Max TPS (Pre-Consensus) | ~100 | ~65,000 |
|
deep-dive
THE BOTTLENECK
Architectural Implications: How L1s Are Engineering Around RAM
Memory bandwidth is the new computational ceiling, forcing L1 architects to redesign execution environments from first principles.
Memory bandwidth is the bottleneck. CPU and GPU compute has outpaced RAM speed, creating a von Neumann bottleneck where data access, not processing, limits throughput. High-frequency trading and AI workloads already hit this wall; blockchain state access is next.
Execution environments are diverging. EVM's linear, in-memory state access is inefficient. New designs like Fuel's UTXO model and Monad's parallelized EVM separate execution from state I/O, allowing parallel transaction processing that saturates CPU cores without RAM contention.
State growth demands new models. Storing all state in RAM is unsustainable. Solutions like Solana's concurrent Merkle trees and Sui's object-centric storage optimize for sequential writes and localized access, reducing the working set size and improving cache efficiency.
Evidence: Solana's validators require 256GB of RAM, a 4x increase in two years, while Monad's benchmark of 10,000 TPS for simple transfers is predicated on its custom mempool and parallel execution engine decoupling compute from storage.
protocol-spotlight
THE MEMORY WALL
Protocol Architectures: A Bandwidth-Centric View
The bottleneck for high-performance L1s has shifted from compute to data movement. The next generation will be defined by memory architecture.
01
The Problem: The von Neumann Bottleneck
Traditional blockchain VMs (EVM, SVM) treat memory as a slow, serialized storage layer. Every state access is a cache miss, throttling throughput.
- State growth compounds latency, making 10k+ TPS unsustainable.
- Parallel execution hits a wall when all threads queue for the same memory bus.
- This is why Solana validators require 128-256GB of RAM just to keep up.
~100 GB/s
DDR5 Bandwidth
>1M IOPS
NVMe Required
02
The Solution: In-Memory State with Linear Access
Architectures like FuelVM and Aptos Move treat global state as a RAM-resident data structure, enabling deterministic, parallelizable access.
- Bytecode is designed for sequential reads (e.g., UTXO models, Merkle tree branches).
- Enables pipelined execution where data prefetching is predictable.
- Reduces consensus overhead by making state transitions a computation problem, not an I/O problem.
10-100x
Access Speed
Sub-ms
Latency
03
The Trade-off: Hardware Centralization Pressure
Optimizing for memory bandwidth inherently favors validators with high-end, homogeneous hardware, conflicting with geographic decentralization.
- Requires server-grade CPUs with wide memory channels and NVMe storage.
- Creates a capital barrier, pushing validation towards professional data centers.
- This is the core tension between the Solana and Ethereum philosophical split.
$10k+
Node Cost
~10 Regions
Geographic Concentration
04
Monad: Pipelined Execution Engine
Monad explicitly attacks the memory bottleneck via a custom EVM-compatible VM with pipelining, asynchronous I/O, and a parallel execution scheduler.
- Separates execution, consensus, and mempool propagation into concurrent stages.
- Uses a state access prefetcher to hide memory latency.
- Targets 10k+ TPS on the EVM by making the hardware work harder, not the developer.
10,000+
Target TPS
1s
Finality
05
The Next Frontier: Near-Memory Compute
The endgame is hardware where computation moves to the data. Think Processing-in-Memory (PIM) chips or FPGA-accelerated state trees.
- ZK provers are an early example, offloading complex ops to GPUs/ASICs.
- Future L1s may ship with a recommended hardware spec for validators, akin to gaming PCs.
- This could bifurcate chains into performance-tier and decentralization-tier networks.
100x+
Potential Gain
ASIC/FPGA
Hardware Shift
06
Implication for App Developers
Bandwidth-optimized L1s enable new primitives but demand new mental models. State layout becomes a first-order optimization.
- Dense packing and sequential IDs (like in Solana's PDAs) outperform hashmap-based storage.
- Contracts must be designed for parallelizability, avoiding global state contention.
- The Ethereum 'state rent' debate is replaced by a 'state locality' imperative.
10-100x
Gas Efficiency
Critical
Data Structure Choice
counter-argument
THE BOTTLENECK SHIFT
The Obvious Rebuttal (And Why It's Wrong)
The next L1 scaling war will be won by optimizing memory bandwidth, not compute or consensus.
Memory bandwidth is the bottleneck. Modern L1s like Solana and Sui already hit compute limits, but their next-gen peers will be constrained by the speed of data movement between CPU, cache, and RAM.
Parallel execution is a memory game. Aptos' Block-STM and Solana's Sealevel runtime demonstrate that parallelization's gains are capped by the system's ability to fetch and synchronize state data.
Consensus is a solved problem. Innovations like Narwhal-Bullshark (Sui/Aptos) and Solana's Tower BFT provide sub-second finality; the remaining latency is in state access, not message agreement.
Evidence: Monad's benchmark. Monad's pipelined architecture targets 10,000 TPS, a figure directly tied to its custom EVM implementation that optimizes for memory locality and cache efficiency over raw CPU cycles.
FREQUENTLY ASKED QUESTIONS
FAQ: Memory Bandwidth for Builders
Common questions about why memory bandwidth will dictate the next generation of L1s.
Memory bandwidth is the data transfer rate between a processor and its memory, measured in GB/s. It's the critical bottleneck for parallel execution, as seen in Solana's Sealevel runtime, where high bandwidth allows thousands of smart contracts to process transactions simultaneously without congestion.
future-outlook
THE BOTTLENECK
The Memory Wall
The fundamental limit for high-throughput blockchains is not compute or consensus, but the speed of moving data between CPU and memory.
Memory bandwidth is the bottleneck. Modern CPUs execute billions of instructions per second, but they stall waiting for data from RAM. A blockchain's state growth—the expanding ledger of account balances and smart contract storage—exacerbates this. Every transaction must read and write to this state, creating a massive, random-access I/O problem that pure computational speed cannot solve.
Parallel execution hits a wall. Solana's Sealevel and Sui's Move demonstrate that parallel transaction processing is necessary for scale. However, their performance plateaus at memory throughput. Even with 1000 cores, if all threads contend for the same memory bus, you get congestion, not concurrency. This is the hardware reality that software cannot abstract away.
The evidence is in the specs. High-performance L1s like Monad and Sei v2 architect around this constraint. Monad's MonadDB and deferred execution pipeline state accesses to minimize stalls. Sei v2's parallelized EVM uses optimistic concurrency control, but its final throughput is gated by the memory subsystem's ability to validate and commit parallel state changes.
The next architectural shift is memory-centric. Winning L1s will treat RAM as the first-class citizen, not the CPU. This means designs leveraging HBM (High Bandwidth Memory), novel caching hierarchies inspired by Aptos' Block-STM, and data structures that maximize sequential access. The chain that best manages its memory access patterns will define the practical TPS ceiling.
takeaways
THE MEMORY WALL
TL;DR: The Bandwidth Mandate
The next L1 war won't be about raw compute; it will be won by architectures that solve the data movement problem.
01
The Problem: The von Neumann Bottleneck
Traditional blockchain VMs treat memory as a slow, sequential storage layer. Every opcode fetch and state read/write creates latency, capping throughput at ~10k TPS for even the most optimized EVM chains. The bottleneck isn't the CPU, it's the bus.
- State Access is the Killer: Over 70% of execution time is spent on SLOAD/SSTORE.
- Parallelism is Blocked: Concurrent execution stalls waiting for shared memory access.
70%
Time on State
~10k
TPS Ceiling
02
The Solution: Parallel Processing with Local Memory
Architectures like Solana and Sui's Move treat memory as a first-class citizen. By using local memory models and explicit data dependencies, they enable genuine parallel execution.
- Solana's Sealevel: Transactions declare read/write sets upfront, allowing the scheduler to run non-conflicting txns in parallel.
- Sui's Object-Centric Model: Each object has a unique ID, eliminating global state contention.
- Result: Theoretical throughput scales with cores, not clock speed.
50k+
Peak TPS
10-100x
Efficiency Gain
03
The New Benchmark: Data Availability Throughput
High execution speed is meaningless if the chain can't ingest or output data. The real mandate is bandwidth to the user. This is why Ethereum's danksharding and Celestia's modular DA are foundational.
- Danksharding Target: ~1.3 MB/s of guaranteed blob data.
- Modular DA Layers: Decouple execution from consensus/DA, allowing rollups to purchase bandwidth-as-a-service.
- Implication: L1s become bandwidth brokers, not just execution engines.
1.3 MB/s
DA Target
$0.01
Cost/Tx Goal
04
The Hardware Endgame: Silicon-Optimized VMs
The final frontier is designing VMs for modern hardware, not abstract machines. Aptos' Block-STM and Fuel's UTXO model are built for pipelining and cache locality.
- Block-STM: Uses software transactional memory for optimistic parallelism, re-executing only conflicts.
- Fuel VM: Minimizes state accesses by design, keeping working data in CPU cache.
- Outcome: Maximizes utilization of L1/L2/L3 CPU caches, reducing trips to main RAM.
100k+
Theoretical TPS
µs
Cache Latency
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