Qwen3.8 27B
Qwen3.8 27B needs 18 GB of VRAM at Q4_K_M with 8k context, computed from its config.json as checked on 3 November 2026. The smallest card that holds it has 24 GB, and the fastest desktop card that runs it, the RTX 5090, generates about 78 tokens per second.
- Parameters
- 27.8B
- Context
- 262,144 tokens
- Licence
- Apache 2.0
- Released
- August 2026
- Config checked
- 3 November 2026
Open Qwen3.8 27B in the LLM checker
Which card size runs it
18 GB against each memory size. A model may use 95% of a card, and only what is left over, or missing, is coloured; tight means above 85%, with little room for a longer conversation.
Other quantisations, context lengths or a Q8 KV cache: the VRAM calculator.
Computed 3 November 2026Which GPU to pick
The smallest card here that loads it at Q4_K_M with 8k of context is the RX 7900 XT (20 GB): 18 GB needed, an estimated 35 tokens per second, and a tight fit that leaves little for the conversation. For room to work, meaning 32,768 tokens of context while staying under 85% of memory, step up to the RTX 5090 Laptop GPU (24 GB), which needs 19.6 GB for that and generates at about 39 tokens per second.
At Q8_0, which is near-lossless, the card is the RTX A6000 (48 GB), needing 31.8 GB. On Apple silicon, a Mac mini M4 Pro holds it with 32,768 tokens of context at an estimated 12 tokens per second; prompt processing is slower than on Nvidia, which long documents make obvious. Among unified-memory desktops, the Ryzen AI Max+ 395 holds it at 32,768 tokens in 19.6 GB, at an estimated 11 tokens per second.
Speed on each card
| GPU | Fits | Est. tokens/s | Feels like | Memory | Bandwidth |
|---|---|---|---|---|---|
| H100 SXM | Yes | ≤ 145 | faster than you can read | 80 GB | 3,350 GB/s |
| A100 | Yes | 89 | faster than you can read | 80 GB | 2,039 GB/s |
| RTX 5090 | Yes | 78 | faster than you can read | 32 GB | 1,792 GB/s |
| RTX PRO 6000 Blackwell | Yes | 78 | faster than you can read | 96 GB | 1,792 GB/s |
| RTX PRO 5000 Blackwell | Yes | 58 | comfortable for chat | 48 GB | 1,344 GB/s |
| RTX 4090 | Yes | 44 | comfortable for chat | 24 GB | 1,008 GB/s |
| RX 7900 XTX | Yes | 42 | comfortable for chat | 24 GB | 960 GB/s |
| RTX 6000 Ada | Yes | 42 | comfortable for chat | 48 GB | 960 GB/s |
| RTX 3090 | Yes | 41 | comfortable for chat | 24 GB | 936 GB/s |
| RTX 5090 Laptop GPU | Yes | 39 | comfortable for chat | 24 GB | 896 GB/s |
| RTX PRO 4500 Blackwell | Yes | 39 | comfortable for chat | 32 GB | 896 GB/s |
| L40S | Yes | 38 | comfortable for chat | 48 GB | 864 GB/s |
| RX 7900 XT | Tight | 35 | comfortable for chat | 20 GB | 800 GB/s |
| RTX A6000 | Yes | 33 | comfortable for chat | 48 GB | 768 GB/s |
| RTX PRO 4000 Blackwell | Yes | 29 | comfortable for chat | 24 GB | 672 GB/s |
| Radeon AI PRO R9700 | Yes | 28 | comfortable for chat | 32 GB | 640 GB/s |
| Arc Pro B70 | Yes | 26 | comfortable for chat | 32 GB | 608 GB/s |
| Arc Pro B60 | Yes | 20 | usable, a little slow | 24 GB | 456 GB/s |
| RTX PRO 4000 Blackwell SFF | Yes | 19 | usable, a little slow | 24 GB | 432 GB/s |
| RTX Spark | Yes | 13 | usable, a little slow | 128 GB (102 for the GPU) | 300 GB/s |
| DGX Spark | Yes | 12 | slow; fine for batch jobs | 128 GB (126 for the GPU) | 273 GB/s |
| Ryzen AI Max+ 395 | Yes | 11 | slow; fine for batch jobs | 128 GB (96 for the GPU) | 256 GB/s |
| RTX 4060 Ti | No | - | does not fit | 16 GB | 288 GB/s |
| RTX 5060 Ti | No | - | does not fit | 16 GB | 448 GB/s |
| RTX 5080 | No | - | does not fit | 16 GB | 960 GB/s |
| RTX 5070 Ti | No | - | does not fit | 16 GB | 896 GB/s |
| RTX 4080 SUPER | No | - | does not fit | 16 GB | 736 GB/s |
| RX 9070 XT | No | - | does not fit | 16 GB | 640 GB/s |
| RX 9060 XT | No | - | does not fit | 16 GB | 320 GB/s |
| RTX 3060 | No | - | does not fit | 12 GB | 360 GB/s |
| RTX 4070 | No | - | does not fit | 12 GB | 504 GB/s |
| RTX 5070 | No | - | does not fit | 12 GB | 672 GB/s |
| Arc B580 | No | - | does not fit | 12 GB | 456 GB/s |
Single-stream decode, from memory bandwidth at 70% efficiency. Prompt processing and batching not included. These are ceilings, not measurements: Qwen3.8 27B reads only 16.1 GB of weights per token at Q4, so little that the per-token costs the formula leaves out decide much of the real speed. The figure marked ≤ is where a real runtime falls furthest below the number shown. See the speed estimator for other quantisations and Apple hardware, or every card compared if you are choosing hardware rather than a model.
Computed 3 November 2026Memory by quantisation and context
| Quant | 4,096 ctx | 8,192 ctx | 32,768 ctx |
|---|---|---|---|
| Q8_0 | 31.6 GB · fits 48 GB | 31.8 GB · fits 48 GB | 33.4 GB · fits 48 GB |
| Q5_K_M | 21.4 GB · fits 24 GB | 21.7 GB · fits 24 GB | 23.3 GB · fits 32 GB |
| Q4_K_M | 17.7 GB · fits 24 GB | 18 GB · fits 24 GB | 19.6 GB · fits 24 GB |
Weights at this quant: 16.1 GB at Q4. Every extra 1,000 tokens of context adds about 0.066 GB of KV cache at FP16, on top of a fixed 0.15 GB of recurrent state that does not grow. Try other settings in the VRAM calculator.
About the model
Qwen3.8 27B has 27.8B parameters. It has 64 layers, of which only 16 are full attention (4 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 48 are Gated DeltaNet linear-attention layers with a fixed state of about 0.15 GB. Alibaba's dense 27B from August 2026, and one of the most downloaded open models of the year. A native vision-language model built on the Qwen3.5 hybrid architecture, aimed at coding, research and long multi-step agent work.
Text, images and video 262,144-token window Apache 2.0 August 2026
Reasoning. On by default and can be switched off per request; reasoning depth is tunable with a reasoning-effort setting.
The parameter count includes the vision encoder; a text-only GGUF is slightly smaller.
Native context window: 262,144 tokens. Licence: Apache 2.0, a permissive licence. Weights published in August 2026. It follows Qwen3.6 27B, Qwen3 32B.
Good at
- The 24 GB card. It fits at Q4 with tens of thousands of tokens of context left, where its predecessor, Qwen3 32B, filled the same card with weights.
- Agentic coding and long tasks, which is what the release notes say it was tuned for.
- Reading images, documents and long videos without a second model.
- Apache 2.0.
Watch out for
- Thinking is on by default, and thinking tokens fill the context like any others. Turn it off for quick questions.
- The memory figures assume a runtime with Gated DeltaNet support. On an older build, expect the 2025 arithmetic.
- At Q8 it needs a 48 GB card; there is no 32 GB middle ground.
Similar models
The models that need about the same memory as Qwen3.8 27B at Q4_K_M and 8k, which makes them the real alternatives on whatever card you have. The context column is what each extra 1,000 tokens costs, and it is where models of the same size differ most.
| Model | Size | Needs | Per 1k context | Window | Released |
|---|---|---|---|---|---|
| Qwen3.8 27B | 27.8B | 18 GB | 0.066 GB | 256k | 2026-08 |
| Qwen3.6 27B | 27.8B | 18 GB | 0.066 GB | 256k | 2026-04 |
| Gemma 3 27B | 27.4B | 18.1 GB | 0.082 GB | 128k | 2025-03 |
| Muse Glimmer 30B | 29.8B | 18.7 GB | 0.013 GB | 128k | 2026-08 |
| Gemma 4 26B-A4B (MoE) | 25.8B · 3.8B active | 16.4 GB | 0.02 GB | 256k | 2026-03 |
See every model compared, which models fit on 8 to 96 GB, or the cheapest hour of every cloud card.
Questions
- Can I run Qwen3.8 27B on a 24 GB card?
- Yes, at Q4_K_M and 8k context it needs about 18 GB, which fits a 24 GB card with room.
- How much VRAM does Qwen3.8 27B need at Q8?
- About 31.8 GB at 8k context, or 33.4 GB at 32k. Q8 is near-lossless; use it when it fits.
- What is the context length of Qwen3.8 27B?
- 262,144 tokens natively. Holding all of it at Q4_K_M takes about 34.6 GB, of which 17.3 GB is context.
- Why does Qwen3.8 27B need so little memory for long context?
- Because most of its layers do not keep a cache that grows. It has 64 layers, of which only 16 are full attention (4 KV heads of dimension 256) and keep a cache that grows with the conversation; the other 48 are Gated DeltaNet linear-attention layers with a fixed state of about 0.15 GB. At 32k tokens the context costs about 2.3 GB; if all 64 layers kept a conventional full-length cache, the same conversation would cost about 8.6 GB.
- Can Qwen3.8 27B run on an RTX 4090 or 3090?
- Yes, at Q4_K_M, and with a long context. Only 16 of its 64 layers keep a cache that grows, so the conversation costs a fraction of what it does on a standard 27B to 32B model. The tables above show the exact figures for both cards.
- How fast is Qwen3.8 27B on an RTX 4090?
- At most about 44 tokens per second at Q4, single stream, which is comfortable for chat. That is a ceiling worked out from the card's memory bandwidth, not a measurement.
- Architecture values from config.json in Qwen/Qwen3.8-27B on Hugging Face, checked 3 November 2026. Every figure on this page is computed from those values; check them before buying hardware for this model.
- Estimates assume a single conversation on a card that is otherwise free. A desktop on the same GPU takes 0.5 to 2 GB.