SOLAR 10.7B Instruct v1.0 uncensored needs ~12.2 GB VRAM. RTX PRO 4500 Blackwell 32GB has 32.0 GB. With Q4_K_M quantization, expect ~115 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
Fit status
Runs well
Decode
115.3 tok/s
TTFT
1679 ms
Safe context
269K
Memory
12.2 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 115.3 tok/s | 916 ms | 269K |
| Coding | C | Runs well | 115.3 tok/s | 1679 ms | 269K |
| Agentic Coding | C | Runs well | 115.3 tok/s | 2442 ms | 269K |
| Reasoning | C | Runs well | 115.3 tok/s | 1984 ms | 269K |
| RAG | C | Runs well | 115.3 tok/s | 3053 ms | 269K |
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 RTX PRO 4500 Blackwell 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C43 |
Q3_K_S | 3 | 5.2 GB | Low | C44 |
NVFP4 | 4 | 6.0 GB | Medium | C44 |
Q4_K_M | 4 | 6.5 GB | Medium | C44 |
Q5_K_M | 5 | 7.7 GB | High | C45 |
Q6_K | 6 | 8.8 GB | High | C45 |
Q8_0 | 8 | 11.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | C49 |
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 startYes, RTX PRO 4500 Blackwell 32GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 115.3 tok/s.
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 12.2 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 RTX PRO 4500 Blackwell 32GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 115.3 tokens per second decode speed with a time-to-first-token of 1679ms using Q4_K_M quantization.
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on RTX PRO 4500 Blackwell 32GB receives a C grade with 115.3 tok/s and 269K context.
On RTX PRO 4500 Blackwell 32GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 269K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
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