View source for LM/Qwen 3.8 27B

== 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.

{| class="wikitable" style="text-align:center;"
! rowspan="2" | Model
! rowspan="2" | Last-Modified
! rowspan="2" | W
! rowspan="2" | A
! colspan="2" | TP2 / C1
! colspan="2" | TP2 / C2
! colspan="2" | TP2 / C4
! colspan="2" | TP2 / C8
! colspan="2" | Single
|-
! PP 512 || TG 128 || PP 512 || TG 128 || PP 512 || TG 128 || PP 512 || TG 128 || PP 512 || TG 128
|-
| style="font-size:10pt; text-align:left;" | cyankiwi/Swift-1.5-Qwen3.8-27B-AWQ-BF16-INT4
| 2026-10-19 || 4 || 16
| 1737 || 65 || 2087 || 97 || 2121 || 139 || 2664 || 240
| ||
|-
| style="font-size:10pt; text-align:left;" | cyankiwi/Swift-1.5-Qwen3.8-27B-AWQ-INT4
| 2026-10-19 || 4 || 16
| 1908 || 79 || 2062 || 115 || 2196 || 158 || 2632 || 269 
| ||
|-
| style="font-size:10pt; text-align:left;" | Launch80/Swift-1.5-Qwen3.8-27B-Uncensored-Int4-baked
| 2026-10-13 || 4 || 16
| 1899 || 102 || 2084 || 165 || 2595 || 241 || 2758 || 307
| ||
|-
| style="font-size:10pt; text-align:left;" | davetha/Huihui-Qwen3.8-27B-abliterated-AutoRound-Int4-baked-embed-int8
| 2026-10-09 || 4 || 16
| 2026 || 118 || 2411 || 164 || 2801 || 266 || 2785 || 319
| ||
|-
| style="font-size:10pt; text-align:left;" | 🔥 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
| ||
|}

=== Available Requests ===

<pre>
/openapi.json, Methods: GET, HEAD
/docs, Methods: GET, HEAD
/docs/oauth2-redirect, Methods: GET, HEAD
/redoc, Methods: GET, HEAD
/load, Methods: GET
/version, Methods: GET
/health, Methods: GET
/metrics, Methods: GET
/tokenize, Methods: POST
/detokenize, Methods: POST
/v1/models, Methods: GET
/ping, Methods: GET
/ping, Methods: POST
/invocations, Methods: POST
/v1/chat/completions, Methods: POST
/v1/chat/completions/batch, Methods: POST
/v1/responses, Methods: POST
/v1/responses/{response_id}, Methods: GET
/v1/responses/{response_id}/cancel, Methods: POST
/v1/completions, Methods: POST
/v1/messages, Methods: POST
/v1/messages/count_tokens, Methods: POST
/generative_scoring, Methods: POST
/scale_elastic_ep, Methods: POST
/is_scaling_elastic_ep, Methods: POST
</pre>

=== hf operations ===

<syntaxhighlight lang="bash">
# 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" 

# list / download / tree
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 
</syntaxhighlight>

=== Models to be experimented on ===

* {{ref model|name=ulkaa/Qwen3.8-27B-AWQ-INT4}}

== llama.cpp ==

* https://unsloth.ai/docs/models/qwen3.8
* https://unsloth.ai/docs/basics/dynamic-3.0-ggufs

=== Running ===

<syntaxhighlight lang="bash">
# 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
</syntaxhighlight>

=== Benchmark for me ===

{| class="wikitable"
! 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
|}

<syntaxhighlight lang="bash">
# 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

# 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
</syntaxhighlight>

=== hf operations ===

<syntaxhighlight lang="bash">
# search
hf models ls --apps llama.cpp --sort downloads --no-truncate \
  --expand "downloads,likes,lastModified" \
  --limit 10 --search "Qwen3.8-27B"

# list remote tree
hf models ls --tree -R -h unsloth/Qwen3.8-27B-GGUF

# download quantization
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_XL*"
hf download unsloth/Qwen3.8-27B-GGUF --include "*Q4_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"

# list local tree
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
</syntaxhighlight>