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Qwen3.8 27B
Quant Q8_0Params 27.3BSize 29.0 GBArch qwen35 27B
on 2× AMD Instinct MI60 / MI50 · ROCm
Generation
18.67tok/s
over 512 tokens
Prompt
274.95tok/s
over 2048 tokens
Model
- Quant
- Q8_0
- Parameters
- 27.3B
- File size
- 29.0 GB
- Architecture
- qwen35 27B
- Variant
- sharpChatTemplate
- File
- Qwen3.8-27B-Q8_0-sharpChatTemplate.gguf
Machine
- Devices
- 2× AMD Instinct MI60 / MI50
- Backend
- ROCm
- CPU
- Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz
How it ran
- Engine
- llama.cpp
- Build
- b10811 (4e6a72bda)
- KV cache
- q8_0
- Flash attention
- on
- GPU layers
- all
- Batch / ubatch
- 8192 / 2048
- Threads
- 6
- Split mode
- tensor
- Tensor split
- 1.00
- Context depth
- 0 (empty context)
Benched by anonymous · Sep 2 · run at 2026-09-02 09:14:16 UTC
The benchmark exactly as it was pasted
ggml_cuda_init: found 2 ROCm devices (Total VRAM: 65504 MiB):
Device 0: AMD Instinct MI60 / MI50, gfx906:sramecc+:xnack- (0x906), VMM: no, Wave Size: 64, VRAM: 32752 MiB
Device 1: AMD Instinct MI60 / MI50, gfx906:sramecc+:xnack- (0x906), VMM: no, Wave Size: 64, VRAM: 32752 MiB
[
[shexp-split] splitting shared experts across the TP group (LLAMA_SHEXP_SPLIT=0 to disable)
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"gpu_info": "AMD Instinct MI60 / MI50, AMD Instinct MI60 / MI50",
"backends": "ROCm",
"model_filename": "/models/Qwen3.8-27B-Q8_0-sharpChatTemplate.gguf",
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"tensor_split": "1.00",
"tensor_buft_overrides": "none",
"load_mode": "dio",
"lazy_mode": "auto",
"embeddings": false,
"no_op_offload": 0,
"no_host": false,
"no_repack": false,
"fit_target": 0,
"fit_min_ctx": 0,
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"n_gen": 0,
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"avg_ns": 7448695478,
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"samples_ns": [ 7448695478 ],
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},
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"n_gen": 512,
"n_depth": 0,
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"stddev_ns": 0,
"avg_ts": 18.666927,
"stddev_ts": 0.000000,
"samples_ns": [ 27428188867 ],
"samples_ts": [ 18.6669 ]
},
{
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