SOLAR 10.7B Instruct v1.0 uncensored needs ~9.5 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~9 tok/s.
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
1.5 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1 GB host RAM)
Decode
8.5 tok/s
TTFT
22851 ms
Safe context
4K
Memory
9.5 GB / 8.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.6 GB host RAM) | 9.8 tok/s | 10726 ms | 4K |
| Coding | D | Very compromised (needs ~1 GB host RAM) | 8.5 tok/s | 22851 ms | 4K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 43662 ms | 4K |
| Reasoning | D | Very compromised | 8.5 tok/s | 27006 ms | 4K |
| RAG | F | Too heavy | 6.4 tok/s | 54578 ms | 4K |
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C53 |
Q3_K_SBest for your GPU | 3 | 5.2 GB | Low | C53 |
NVFP4 | 4 | 6.0 GB | Medium | F0 |
Q4_K_M | 4 | 6.5 GB | Medium | F0 |
Q5_K_M | 5 | 7.7 GB | High | F0 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Copy-paste commands to run SOLAR 10.7B Instruct v1.0 uncensored on your machine.
Run
lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start升级选项
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 201%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 264%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 367%.
~$449 MSRP
Yes, RX 580 8GB can run SOLAR 10.7B Instruct v1.0 uncensored with a D grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 8.5 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 9.5 GB of memory with Q4_K_M quantization.
The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.
On RX 580 8GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 8.5 tokens per second decode speed with a time-to-first-token of 22851ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RX 580 8GB receives a D grade with 8.5 tok/s and 4K context.
On RX 580 8GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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