Inference Optimization for Large-Scale Model Architectures
JAX & Accelerated Serving
Performance cost-improvements for popular large-model architectures using JAX/XLA, quantisation and sharding.
Problem Statement
Large-scale transformer models — dense, MoE or sparsely-activated — face major serving challenges: high latency per token, large hardware/memory cost, limited throughput for many users. Our aim: serve these models at scale — many users, long contexts, variable prompts — while keeping latency low and cost per query reasonable.
High Latency
Unoptimized models suffer from redundant compute and memory transfers.
Hardware Cost
Serving 70B+ models on GPUs costs orders of magnitude more than necessary.
Limited Throughput
Naive serving can't sustain high concurrency without latency blowup.
Our Approach
Four technical pillars that combine to deliver production-ready JAX inference on GPU and TPU at dramatically lower cost.
JAX + XLA Kernel Fusion
Fuse kernels, reduce overhead, and optimise execution graphs for large-scale transformer inference. Compilation is backend-aware — TPU and GPU paths are optimized independently.
Quantisation (int8/FP8)
Reduce KV-cache memory and bandwidth usage to achieve higher efficiency and longer context handling — with less than 1% precision loss on financial benchmarks.
Multi-Device Sharding
Shard model parameters, activations, and KV caches across GPU/TPU pods using jax.pmap and shard_map for expert and tensor parallelism.
Agent-Based Optimisation
Autonomous agents automatically convert, analyze, and re-structure model execution code — bridging PyTorch/TensorFlow definitions to optimized JAX graph execution.
Why This Matters
Lower latency
Smoother interactive experience for end users.
Higher throughput
More users per dollar, less idle hardware.
Lower cost
Proven hardware specific cost optimizations per model.
Production-ready
JAX stack deployable on GPUs and TPUs today.
Measured Efficiency Gains: TPU vs GPU
Real benchmark results from vLLM with 1,000 concurrent prompts and identical model checkpoints — no post-processing.
Llama-3 1B
TPU v5e-1 vs T4 GPU
Llama-3 3B
TPU v5e-1 vs T4 GPU
Llama-3.1-8B
TPU v6e-1 vs A100
Llama-3.3-70B
TPU v6e-8 vs 2×H200
Conclusion
By leveraging AI agents purpose-built for code translation and optimization, we unlock the full performance potential of JAX on TPU — automatically. Through intelligent kernel fusion (JAX/XLA), advanced quantization, and dynamic sharding of models and KV caches, these agents eliminate manual bottlenecks and continuously optimize execution paths in real time.
Ready to Cut Your Inference Costs?
See how Siaivo automatically migrates your LLM serving stack to JAX on TPU — no manual porting required.
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