Baichuan M3 235B needs ~172.6 GB but RX 590 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
164.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
108324 ms
Safe context
4K
Memory
172.6 GB / 8.0 GB
Offload
100%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 172.6 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 59086 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 108324 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 157562 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 128019 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 196952 ms | 4K |
How Baichuan M3 235B (235B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 91.7 GB | Low | F0 |
Q3_K_S | 3 | 115.2 GB | Low | F0 |
NVFP4 | 4 | 131.6 GB | Medium | F0 |
Q4_K_M | 4 | 143.4 GB | Medium | F0 |
Q5_K_M | 5 | 169.2 GB | High | F0 |
Q6_K | 6 | 192.7 GB | High | F0 |
Q8_0 | 8 | 251.5 GB | Very High | F0 |
F16 | 16 | 481.7 GB | Maximum | F0 |
升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$20,000 MSRP
No, Baichuan M3 235B requires more memory than RX 590 8GB provides.
Baichuan M3 235B (235B parameters) requires approximately 172.6 GB of memory with Q4_K_M quantization.
The recommended quantization for Baichuan M3 235B is Q4_K_M, which balances quality and memory efficiency.
On RX 590 8GB, Baichuan M3 235B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 108324ms using Q4_K_M quantization.
For coding workloads, Baichuan M3 235B on RX 590 8GB receives a F grade with 2.0 tok/s and 4K context.
On RX 590 8GB, Baichuan M3 235B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--baichuan-m3-235b-gguf-on-rx-590-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
