平头哥 含光 800
PCIE 在售 发布于 2019 hanguang-gen1
BF16
TFLOP/s
—
FP8
TFLOP/s
不支持
FP4
TFLOP/s
不支持
Memory
GB
16 厂商声称
Mem BW
GB/s
256 厂商声称
TDP
W
280 厂商声称
完整规格
算力
FP4 TFLOPS
不支持
FP8 TFLOPS
不支持
BF16 TFLOPS
—
FP16 TFLOPS
25
INT8 TOPS
825
显存
容量
16 GB
带宽
256 GB/s
类型
LPDDR5
芯片架构 🟢 vendor floorplan
Cluster count
4
制程
12 nm
Transistors
17 B
PCIe
Gen 4 ×16
Scale-Up (节点内)
协议
PCIe-Gen4
单链带宽
64 GB/s
World size
4
拓扑
pcie-fabric
交换机
—
Scale-Out (节点间)
单卡出口
100 Gbps
协议
RoCEv2
NIC
—
拓扑示意
拓扑结构 · Topology
4 卡 scale-up domain
芯片内部 / Die-level architecture
⚠ illustrative / 示意性版图: compute-unit and HBM-stack count are inferred from public BF16 / memory specs. architecture field not populated for this card yet. Contribute floorplan data →
集群拓扑 / Cluster topology · PCIe-Gen4 @ 64 GB/s
Scale-Up · 域内
PCIe-Gen4
64 GB/s · 拓扑: pcie-fabric
world_size = 4
Scale-Out · 跨域
RoCEv2
100 Gbps/卡 NIC
能跑哪些模型?
Quick estimates · decode tok/s/card 上界
TP=4 · INT8 · 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 | 3 | 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 |
算子级 fit · 任意模型瓶颈类型 + 上界
算子级 fit · operator-level fit (per-token roofline)
基于每个模型 operator_decomposition + 本卡 BF16 0 TFLOPS / 256 GB/s 计算 · ridge point ≈ 0 FLOPs/byte
| 模型 | domain | 主导算子 | AI · F/B | 瓶颈 | tok/s 上界 |
|---|---|---|---|---|---|
| DeepSeek V4 Flash | llm | matmul | 0.8 | 🔥 计算 | — |
| DeepSeek V4 Pro | llm | matmul | 245.5 | 🔥 计算 | — |
| Kimi K2.6 | llm | matmul | 0.8 | 🔥 计算 | — |
| MiniMax M2.7 | llm | matmul | 0.6 | 🔥 计算 | — |
| GLM-5.1 | llm | matmul | 0.8 | 🔥 计算 | — |
| Qwen3.6 Plus | llm | matmul | 0.7 | 🔥 计算 | — |
| Mistral Small 4 | llm | matmul | 0.6 | 🔥 计算 | — |
| GLM-5 Reasoning | llm | matmul | 0.9 | 🔥 计算 | — |
需要 efficiency 校准 + concurrency 扫描 + TCO 估算 → 在计算器中评估 →
算子支持 & 优化空间
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 🟡 partial
small scale-up domain; expert-parallel needs careful sharding
AllReduce 🟢 mature
PCIe-Gen4 ring all-reduce
Attention
Multi-Head Attention 🟡 partial
no production attention engine
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 🔴 gap
no MoE-aware engine
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。
软件栈支持
| 引擎 | 状态 | BF16 | FP16 | FP4 | FP8 E4M3 | FP8 E5M2 | INT4 AWQ |
|---|---|---|---|---|---|---|---|
| HanGuangAI | 官方 | — | — | — | — | — | — |
| LMDeploy | 未确认 | — | — | — | — | — | — |
| MindIE | 未确认 | — | — | — | — | — | — |
| MoRI | 未确认 | — | — | — | — | — | — |
| SGLang | 未确认 | — | — | — | — | — | — |
| TensorRT-LLM (Dynamo) | 未确认 | — | — | — | — | — | — |
| vLLM | 未确认 | — | — | — | — | — | — |
已有部署案例 (0)
暂无该硬件的实测案例。
成为第一个贡献者?
引证
- [1] T-Head HanGuang 800 launch coverage (Alibaba Cloud Apsara 2019) — https://www.t-head.cn/ · 访问于 2026-04-28 厂商声称
- [2] HanGuang 800 (含光800): 4-cluster NPU, 17B transistors @ TSMC 12nm; INT8-focused inference accelerator (Apsara 2019 launch) — https://www.t-head.cn/ · 访问于 2026-04-28 社区估算
⚠ HanGuang 800 is INT8 inference-focused; not designed for FP training.
⚠ Specs are vendor-claimed; LLM inference support is not the primary use case.