Yes — with 13.7 GB to spare
Qwen3.8 27B at Q4_K_M fits your GeForce RTX 5090 entirely on the GPU at 8K context, at an estimated 69 tokens per second. Past 226K the KV cache pushes it over — quantise the cache to q8_0, or step down a quantisation, to go longer.
The current default local Qwen: dense 27B, text + image + video, 262K context. Only 16 of its 64 blocks keep a KV cache, so long context is cheap.
The VRAM budget
Quantisation ladder
| Quant | Weights | Total @ 8K | Max context | Tok/s | Quality | Fit |
|---|---|---|---|---|---|---|
| Q8_0 | 27.5 GB | 28.6 GB | 36K | 39 | −0.1% ppl | Long context |
| Q6_K | 21.2 GB | 22.3 GB | 137K | 51 | −0.4% ppl | Long context |
| Q5_K_M | 18.4 GB | 19.5 GB | 183K | 59 | −0.8% ppl | Long context |
| Q4_K_M | 15.6 GB | 16.7 GB | 226K | 69 | −1.9% ppl | Recommended |
| Q3_K_M | 12.7 GB | 13.8 GB | 256K | 86 | −5.4% ppl | Long context |
| Q2_K | 10.8 GB | 11.9 GB | 256K | 100 | −15% ppl | Long context |
Quality is the published perplexity delta against f16 weights. Max context assumes an f16 KV cache; q8_0 roughly doubles it. Only 16 of its 64 blocks keep a per-token KV cache; the rest are linear-attention, Mamba or convolution blocks with a fixed-size state.
How to run it
$ ollama pull qwen3.8:27b $ OLLAMA_CONTEXT_LENGTH=8192 \ ollama run qwen3.8:27b
The default. One binary, a model registry, an OpenAI-compatible port. More on Ollama.