CPU Instruction Sets for LLM Inference: AVX, AMX, SME vs GPUs

1. Introduction

Large Language Models (LLMs) have historically been deployed on GPUs due to their high throughput for dense linear algebra operations. However, supply constraints, energy consumption, and cost per token have pushed both industry and research communities to revisit CPUs as viable inference engines — particularly when augmented with new instruction sets like AVX (Advanced Vector Extensions), AMX (Advanced Matrix Extensions), and SME (Scalable Matrix Extension).

These ISA (Instruction Set Architecture) extensions provide specialized matrix/vector operations that map directly to transformer workloads, especially post-quantization. As a result, CPUs are gaining renewed interest as scalable, energy-efficient alternatives for small-to-medium model inference, pre/post-processing, and even some server-side LLM workloads.

2. The Instruction Sets

AVX / AVX-512 (Intel & AMD, x86)

  • SIMD (vector) extensions, widening registers to accelerate dot-products and vector operations.

  • AVX-512 VNNI and BF16 instructions target INT8 and BF16 workloads directly.

  • Adoption: Present in Intel Xeon Scalable, AMD EPYC Zen 4/5. Widely used in llama.cpp, llamafile, Hugging Face Optimum CPU kernels.

  • Role: Boosts throughput for quantized matmuls, attention blocks, and pre/post-processing.

AMX (Intel Advanced Matrix Extensions, x86)

  • Tile-based matrix multipliers built into Intel 4th Gen Xeon (Sapphire Rapids) and beyond (Granite Rapids / Xeon 6).

  • Optimized for INT8 and BF16 matmuls (core of transformer workloads).

  • Software: Integrated in oneDNN, PyTorch (via mkldnn backend), and OpenVINO.

  • Adoption: Intel, AWS EC2 m7i/m7i-flex instances; enterprise inference stacks.

  • Role: Reduces latency & boosts throughput on quantized LLMs running on Xeon.

SME / SME2 (Arm Scalable Matrix Extensions, Armv9.x)

  • Arm’s equivalent of AMX: tile-style matrix ISA, augmenting SVE/SVE2.

  • SME2 (Armv9.3) refines memory and vectorization for transformer ops.

  • Software: Enablement via KleidiAI, ONNX Runtime, Android integration announced (2025).

  • Adoption: Emerging in Armv9 client CPUs (e.g., Apple M4, mobile SoCs) and AWS Graviton4 (Armv9 SVE; SME2 soon).

  • Role: Brings competitive perf/Watt for inference on cloud Arm servers and on-device AI.

3. Why CPUs Matter for LLM Inference

  1. Availability & Cost CPUs are abundant, cheaper, and not subject to GPU shortages.

  2. Perf/Watt Efficiency Modern Xeon/EPYC cores with AMX/AVX-512 run quantized models at lower joules/token compared to GPUs at small batch sizes.

  3. Memory Access CPUs can leverage larger system memory, useful for models with large parameter footprints or long context windows.

  4. Software Ecosystem Major frameworks (PyTorch, ONNX Runtime, OpenVINO) now map automatically to AVX/AMX/SME backends.

  5. Quantization Synergy The industry trend toward INT8 / BF16 / 4-bit quantization aligns perfectly with AMX and SME instructions.

4. Current Adoption & Industry Players

  • Intel: AMX is shipping in Xeon Scalable (Sapphire/Granite Rapids). Integrated into PyTorch (via oneDNN) and OpenVINO. Benchmarks published for LLaMA-2/3.

  • AMD: AVX-512 + VNNI + BF16 in Zen 4/5 EPYC. llama.cpp optimized paths show tokens/sec boosts vs AVX2. AMD positions EPYC as GPU-light inference solution.

  • Arm: SME2 announced with Armv9.3, Android integration in 2025, and cloud adoption (Graviton4). Apple M4 benchmarks show strong uplift in FP32 matmuls.

  • Cloud Providers:

    • AWS Graviton4: SVE/BF16/INT8; SME2 support in roadmap.

    • Azure & GCP: Xeon AMX instances available for LLM workloads.

5. Benchmarks & Comparisons

CPU Benchmarks with AMX / AVX

  • OpenMetal (Xeon AMX):

    • LLaMA-3 3.2B INT8 → ~57 tokens/sec (AMX on) vs ~28 t/s (AMX off).

    • With 4-bit quantization → ~80 t/s possible.

  • Presidio (AWS m7i with Xeon AMX):

    • Generic prompts: ~100 t/s with AMX vs ~25 t/s baseline.

    • RAG prompts: ~120 t/s with AMX vs ~35–40 t/s without.

  • llama.cpp on AMD EPYC AVX-512:

    • Significant uplift vs AVX2; practical for local/agent workloads.

Arm SME / SME2

  • Apple M4 (SME microbenchmarks):

    • 2.3 TFLOPS FP32 matmul throughput.

    • Outperforms vendor BLAS for small matrix ops.

  • Arm Lumex CSS (SME2 reference):

    • Up to 5× AI perf uplift vs prior gen CPUs.

    • 4.7× lower latency in speech workloads.

CPU vs GPU Gap

  • Small Models (~3B, quantized):

    • CPU AMX → ~50–100 t/s.

    • GPU (A100/H100) → 1000+ t/s.

    • Gap: ~10×.

  • Medium Models (~20–70B):

    • CPU → tens of t/s.

    • GPU → hundreds–thousands t/s.

    • Gap: 5–30× depending on precision & batch.

  • First Token Latency:

    • CPU AMX: ~100–200ms.

    • GPU: ~10–50ms.

  • Perf/Watt / Cost:

    • CPU competitive at small batch sizes & intermittent workloads.

    • GPU wins at scale & high concurrency.

6. Future Outlook

  • AMX will dominate Intel’s CPU inference story, aligned with INT8/BF16 quantization and software ecosystem (PyTorch, OpenVINO).

  • AVX-512 will remain a strong optimization path on AMD EPYC and Intel consumer CPUs, especially for lightweight inference (agents, RAG pipelines).

  • SME2 positions Arm for on-device AI and eventually cloud-scale LLM inference as Neoverse V3/V4 cores adopt it.

  • GPU vs CPU roles will bifurcate:

    • GPUs: High throughput, very large models, training & dense inference.

    • CPUs: Quantized mid-tier inference, tokenization, edge, cost-sensitive serving.

    • NPUs / accelerators: May bridge gaps, but CPUs offer universal deployment.

7. Key Takeaways

  1. AMX and SME2 are the future of CPU inference. They bring GPU-like matmul performance into CPUs, tightly aligned with quantization trends.

  2. Big corps already betting: Intel (AMX), AMD (AVX-512), Arm (SME2), AWS (Graviton4), Apple (M4 SME).

  3. Benchmark reality: GPUs are still 5–30× faster for large LLMs, but CPUs can be cost-competitive for small-to-mid models.

  4. Practical today: Use Xeon AMX / EPYC AVX-512 for mid-tier workloads (3–20B LLMs) with INT8/4-bit quantization.

  5. Emerging tomorrow: Arm SME2 for on-device & cloud AI; watch ecosystem enablement via ONNX Runtime and Android.

8. Suggested Next Steps (if applying to Cortensor / similar infra)

  • Implement ISA-aware scheduling in your router (AVX2 vs AVX-512 vs AMX vs SME).

  • Benchmark INT8 vs 4-bit quantization across CPU/GPU backends for your target models (3B, 7B, 13B, 70B).

  • Deploy Xeon AMX / EPYC AVX-512 nodes in your NodePool for cost-sensitive inference; route massive jobs to GPUs.

  • Track Arm SME2 adoption for mobile/edge nodes (important for global decentralization).

📌 Bottom line:

  • AMX (Intel) and SME2 (Arm) are not GPU killers but GPU complements.

  • They’ll extend LLM inference beyond GPUs, making CPU inference practical, scalable, and cost-efficient for a significant share of workloads.

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