AWS Inferentia 2
PROPRIETARY In production Released 2023 inf2
BF16
TFLOP/s
190 厂商声称
FP8
TFLOP/s
unsupported
FP4
TFLOP/s
unsupported
Memory
GB
32 厂商声称
Mem BW
GB/s
820 厂商声称
TDP
W
175 厂商声称
Full specs
Compute
FP4 TFLOPS
unsupported
FP8 TFLOPS
unsupported
BF16 TFLOPS
190
FP16 TFLOPS
190
INT8 TOPS
380
Memory
Capacity
32 GB
Bandwidth
820 GB/s
Type
HBM2e
Die architecture 🟢 vendor floorplan
XPU count
2
HBM stacks
2
Process
7 nm
Scale-Up (intra-node)
Protocol
NeuronLink
Per-link BW
384 GB/s
World size
12
Topology
ring
Switch
—
Scale-Out (inter-node)
Per-card NIC
100 Gbps
Protocol
EFA
NIC
—
Topology
拓扑结构 · Topology
12 卡 scale-up domain
芯片内部 / Die-level architecture
🟢 vendor floorplan 2 XPUs · 2× HBM · 7 nm
集群拓扑 / Cluster topology · NeuronLink @ 384 GB/s
Scale-Up · 域内
NeuronLink
384 GB/s · 拓扑: ring
world_size = 12
Scale-Out · 跨域
EFA
100 Gbps/卡 NIC
Which models can it run?
Quick estimates · decode tok/s/card 上界
TP=8 · BF16 · batch=16 · prefill=1024 · decode=256 · 已应用 efficiency 校准
| 模型 | 参数 (active) | Decode tok/s/card | 瓶颈 |
|---|---|---|---|
| DeepSeek V4 Pro deepseek | 49B | — | 显存不足 |
| DeepSeek V4 Flash deepseek | 13B | — | 显存不足 |
| Mistral Small 4 mistral | 22B | — | 显存不足 |
| GLM-5 Reasoning zhipu | 32B | 9 | 内存带宽 |
| GLM-5.1 zhipu | 32B | — | 显存不足 |
| Qwen3.6 Plus alibaba | 35B | — | 显存不足 |
| Kimi K2.6 moonshot | 32B | — | 显存不足 |
| MiniMax M2.7 minimax | 46B | — | 显存不足 |
Operator-level fit · per-model bottleneck + upper bound
算子级 fit · operator-level fit (per-token roofline)
基于每个模型 operator_decomposition + 本卡 BF16 190 TFLOPS / 820 GB/s 计算 · ridge point ≈ 232 FLOPs/byte
| 模型 | domain | 主导算子 | AI · F/B | 瓶颈 | tok/s 上界 |
|---|---|---|---|---|---|
| DeepSeek V4 Pro | llm | matmul | 245.5 | 🔥 计算 | 32k |
| GraphCast | scientific | graph-message-passing | 0.9 | 💾 内存带宽 | 1513 |
| AlphaFold 3 | scientific | pair-bias-attention | 2.3 | 💾 内存带宽 | 455 |
| GPT-OSS | llm | matmul | 0.7 | 💾 内存带宽 | 66 |
| Gemma 4 26B | llm | matmul | 0.7 | 💾 内存带宽 | 49 |
| DeepSeek V4 Flash | llm | matmul | 0.8 | 💾 内存带宽 | 47 |
| Mistral Small 4 | llm | matmul | 0.6 | 💾 内存带宽 | 21 |
| Llama 4 Maverick | llm | matmul | 0.8 | 💾 内存带宽 | 21 |
需要 efficiency 校准 + concurrency 扫描 + TCO 估算 → 在计算器中评估 →
Operator support & optimization headroom
算子支持 & 优化空间 / Operator support & headroom
Per-operator support derived from software_support.engines + scale-up topology. Optimization headroom from measured efficiency factor.
Optimization headroom
+50 pp
moderate
No cases yet — using default 0.5 efficiency. Real headroom unknown until first measurement lands.
Communication (collective)
All-to-All 🟢 mature
all-to-all via NeuronLink world_size=12
AllReduce 🟢 mature
NeuronLink ring all-reduce
Attention
Multi-Head Attention 🟢 mature
paged-attention via vLLM/SGLang/MindIE
FlashAttention-3 🔴 gap
No FA-3 path; falls back to FA-2 / vanilla SDPA
Matrix multiply (GEMM)
Matrix Multiplication 🟢 mature
GEMM supported on all inference engines
MoE routing
MoE Routing 🟢 mature
MoE gating supported via vLLM ≥0.4 / SGLang
Normalization
RMSNorm 🟢 mature
fused into engine kernels
Embedding
Rotary Position Embedding 🟢 mature
fused into engine kernels
Activation
SiLU / Swish 🟢 mature
fused into engine kernels
Softmax 🟢 mature
fused into engine kernels
最接近的替代卡 (按规格相似度)
基于 BF16 算力 / 显存 / 显存带宽 / FP8 加权欧氏距离。供选型决策参考。
Software-stack support
| Engine | Status | BF16 | FP16 | FP4 | FP8 E4M3 | FP8 E5M2 | INT4 AWQ |
|---|---|---|---|---|---|---|---|
| HanGuangAI | unconfirmed | — | — | — | — | — | — |
| LMDeploy | unconfirmed | — | — | — | — | — | — |
| MindIE | unconfirmed | — | — | — | — | — | — |
| MoRI | unconfirmed | — | — | — | — | — | — |
| SGLang | unconfirmed | — | — | — | — | — | — |
| TensorRT-LLM (Dynamo) | unconfirmed | — | — | — | — | — | — |
| vLLM | community | ✓ | ✓ | — | — | — | — |
Existing deployment cases (0)
No measured cases yet for this card.
Be the first contributor?
Citations
- [1] AWS Inferentia 2 product page — https://aws.amazon.com/ai/machine-learning/inferentia/ · accessed 2026-04-28 厂商声称
- [2] Inferentia 2: 2 NeuronCore-v2 engines (each tensor + scalar + vector + GPSIMD), 2× HBM2e ⇒ 32 GB; TSMC 7nm-class — https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/arch/neuron-hardware/inferentia2.html · accessed 2026-04-28 厂商声称
⚠ Inferentia2 only available via EC2 Inf2 instances.