SOLAR 10.7B Instruct v1.0 uncensored needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q2_K quantization, expect ~17 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
3.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.9 tok/s
TTFT
28179 ms
Safe context
4K
Memory
9.3 GB / 6.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% 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.
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 0.6 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 8.0 tok/s | 13266 ms | 4K |
| Coding | F | Too heavy | 6.9 tok/s | 28179 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 53519 ms | 4K |
| Reasoning | F | Too heavy | 6.9 tok/s | 33303 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 66899 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for SOLAR 10.7B Instruct v1.0 uncensored at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~150 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 149.8 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 117.4 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 105.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 100.4 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 93.6 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 85.3 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 71.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 67.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 60.8 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 46.4 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 36.8 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 36.4 | Tight |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 33.7 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 28.3 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 16.8 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | F0 |
Q3_K_S | 3 | 5.2 GB | Low | F0 |
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アップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
〜$229 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.
〜$329 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.
〜$349 MSRP
Yes, RX 5600 XT 6GB can run SOLAR 10.7B Instruct v1.0 uncensored at Q2_K quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 9.3 GB which exceeds available memory, but at Q2_K it needs only 6.9 GB. Expected decode speed: 16.9 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 9.3 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at Q2_K using 6.9 GB.
The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is Q2_K, which uses 6.9 GB.
On RX 5600 XT 6GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 16.9 tokens per second decode speed with a time-to-first-token of 11450ms using Q2_K quantization.
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RX 5600 XT 6GB receives a F grade with 6.9 tok/s and 4K context.
On RX 5600 XT 6GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 4K tokens of context at Q2_K quantization. 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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<iframe src="https://willitrunai.com/embed/hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf-on-rx-5600-xt-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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