LM/Qwen 3.8 27B
< LM
vLLM
Best-Fit Models for B70
The B70 delivers the best performance with W4A8 quantized models.
The underlying rationale can be referenced from TechPowerUp’s spec data, and the listed models show solid performance.
| Model | Last-Modified | W | A | TP2 / C1 | TP2 / C2 | TP2 / C4 | TP2 / C8 | Single | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PP 512 | TG 128 | PP 512 | TG 128 | PP 512 | TG 128 | PP 512 | TG 128 | PP 512 | TG 128 | ||||
| cyankiwi/Swift-1.5-Qwen3.8-27B-AWQ-BF16-INT4 | 2026-10-19 | 4 | 16 | 1737 | 65 | 2087 | 97 | 2121 | 139 | 2664 | 240 | ||
| cyankiwi/Swift-1.5-Qwen3.8-27B-AWQ-INT4 | 2026-10-19 | 4 | 16 | 1908 | 79 | 2062 | 115 | 2196 | 158 | 2632 | 269 | ||
| Launch80/Swift-1.5-Qwen3.8-27B-Uncensored-Int4-baked | 2026-10-13 | 4 | 16 | 1899 | 102 | 2084 | 165 | 2595 | 241 | 2758 | 307 | ||
| davetha/Huihui-Qwen3.8-27B-abliterated-AutoRound-Int4-baked-embed-int8 | 2026-10-09 | 4 | 16 | 2026 | 118 | 2411 | 164 | 2801 | 266 | 2785 | 319 | ||
| 🔥 Wrapzii/Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8 🔥 | 2026-10-01 | 4 | 16 | 2335 | 110 | 2429 | 190 | 2822 | 286 | 2928 | 506 | ||
hf operations
# search latest vllm models
hf models ls --apps vllm --sort last_modified --no-truncate --no-gated \
--expand "downloads,likes,lastModified" \
--limit 25 --search "Qwen3.8 27B int4"
hf models ls --apps vllm --sort last_modified --no-truncate --no-gated \
--expand "downloads,likes,lastModified" \
--limit 25 --search "Qwen3.8 27B int8 int4"
hf models ls --apps vllm --sort last_modified --no-truncate --no-gated \
--expand "downloads,likes,lastModified" \
--limit 25 --search "Qwen3.8 27B autoround"
hf models ls --apps vllm --sort last_modified --no-truncate --no-gated \
--expand "downloads,likes,lastModified" \
--limit 25 --search "Qwen3.8 27B w4a8"
# Wrapzii/Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8
hf models ls --tree -R -h Wrapzii/Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8
hf download Wrapzii/Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8
tree -h ~/.cache/huggingface/hub/models--Wrapzii--Swift-1.5-Qwen3.8-27b-GPTQ-Int4-baked-v1-embed-int8
llama.cpp
Running
The five options are important, especially --chat-template-kwargs '{"reasoning_effort":"..."}'.
The option reasoning_effort can be set to xhigh, medium, and low, but xhigh causes significant performance degradation.
--temp 1.0--top-p 0.95--top-k 20--min-p 0.0--chat-template-kwargs '{"reasoning_effort":"low"}'
# works
llama-server -hf unsloth/Qwen3.8-27B-GGUF:UD-Q5_K_XL
--spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.7 -ngld 99
-fa 1 -c 128000 -ctk f16 -ctv f16
--temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0
--chat-template-kwargs '{"reasoning_effort":"low"}'
-b 2048 -ub 1024
-ngl 99 -np 1 -t 6
--load-mode mlock --jinja --reasoning-preserve
Benchmark for me
# control group
./llama.cpp/sycl/bin/llama-bench -hf unsloth/Qwen3.8-27B-GGUF:UD-Q5_K_XL -fa 1 -ctk q8_0 -ctv q8_0
| model | size | params | backend | ngl | type_k | type_v | fa | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | -----: | -----: | --: | --------------: | -------------------: |
| qwen35 27B Q5_K - Medium | 19.43 GiB | 27.32 B | SYCL | 99 | q8_0 | q8_0 | 1 | pp512 | 951.05 ± 7.72 |
| qwen35 27B Q5_K - Medium | 19.43 GiB | 27.32 B | SYCL | 99 | q8_0 | q8_0 | 1 | tg128 | 22.17 ± 0.06 |
# Test -ctv q4_0
./llama.cpp/sycl/bin/llama-bench -hf unsloth/Qwen3.8-27B-GGUF:UD-Q5_K_XL -fa 1 -ctk q8_0 -ctv q4_0
# Test -ub 2048
./llama.cpp/sycl/bin/llama-bench -hf unsloth/Qwen3.8-27B-GGUF:UD-Q5_K_XL -fa 1 -ctk q8_0 -ctv q8_0 -ub 2048
# Test OpenVINO
GGML_OPENVINO_DEVICE=GPU GGML_OPENVINO_STATEFUL_EXECUTION=1 ./llama.cpp/openvino/bin/llama-bench -hf unsloth/Qwen3.8-27B-GGUF:UD-Q5_K_XL -fa 0
Experiment
# search
hf models ls --search "Qwen3.8-27B" --apps llama.cpp --expand "downloads,likes,lastModified" --sort downloads --no-truncate --limit 10
# unsloth
hf models ls --tree -R -h unsloth/Qwen3.8-27B-GGUF
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-IQ4_XS*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q4_K_XL*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q5_K_XL*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q6_K.gguf"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q6_K_M*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q6_K_L*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q6_K_XL*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*UD-Q8_K_L*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*Q4_0*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*Q8_0*"
hf download unsloth/Qwen3.8-27B-GGUF --include "mmproj-BF16*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*mtp-Qwen3.8-27B-Q4_0.gguf"
find ~/.cache/huggingface/hub -name "*Qwen3.8-27B-UD-Q8_K_L.gguf"
find ~/.cache/huggingface/hub -name "*Qwen3.8-27B-UD-Q8_K_L.gguf" -exec sh -c 'rm -f "$1" "$(readlink -f "$1")"' _ {} \;
tree -h ~/.cache/huggingface/hub/models--unsloth--Qwen3.8-27B-GGUF