solar finalised finetuned Model 10.7B i1 needs ~10.6 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~24 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
23.9 tok/s
TTFT
8100 ms
Safe context
85K
Memory
10.6 GB / 16.0 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 | 23.9 tok/s | 4418 ms | 85K |
| Coding | C | Runs well | 23.9 tok/s | 8100 ms | 85K |
| Agentic Coding | C | Runs well | 23.9 tok/s | 11782 ms | 85K |
| Reasoning | C | Runs well | 23.9 tok/s | 9573 ms | 85K |
| RAG | C | Runs well | 23.9 tok/s | 14728 ms | 85K |
Inference speed
Estimated decode speed (tokens/sec) for solar finalised finetuned Model 10.7B i1 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 finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C48 |
Q3_K_S | 3 | 5.2 GB | Low | C49 |
NVFP4 | 4 | 6.0 GB | Medium | C49 |
Q4_K_M | 4 | 6.5 GB | Medium | C50 |
Q5_K_M | 5 | 7.7 GB | High | C51 |
Q6_K | 6 | 8.8 GB | High | C51 |
Q8_0Best for your GPU | 8 | 11.4 GB | Very High | C50 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server startUpgrade options
RTX 3090 24GBBudget pickRaises estimated decode speed by about 320%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX 4090 24GBBest valueRaises estimated decode speed by about 391%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
RTX PRO 4000 Blackwell 24GBNVIDIA upgradeRaises estimated decode speed by about 262%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, NVIDIA A2 16GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Runs well). Expected decode speed: 23.9 tok/s.
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.6 GB of memory with Q4_K_M quantization.
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A2 16GB, solar finalised finetuned Model 10.7B i1 achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8100ms using Q4_K_M quantization.
For coding workloads, solar finalised finetuned Model 10.7B i1 on NVIDIA A2 16GB receives a C grade with 23.9 tok/s and 85K context.
On NVIDIA A2 16GB, solar finalised finetuned Model 10.7B i1 can safely use up to 85K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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