Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+115)
C
Adds memory headroom for longer context windows and future model growth.85.3 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $6,999 MSRP
Can SOLAR 10.7B Instruct v1.0 uncensored run on NVIDIA H200 PCIe 141GB?
YES — Runs Great
C45Usable
Estimated from fit model
SOLAR 10.7B Instruct v1.0 uncensored needs ~23.1 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~150 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 23.1 GB, 149.8 tok/s, Runs well
23.1 GB required141.0 GB available
16% VRAM used
Fit status
Runs well
Decode
149.8 tok/s
TTFT
1292 ms
Safe context
1.5M
Memory
23.1 GB / 141.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom14.1 GB
See how fast it feels
See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored on NVIDIA H200 PCIe 141GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 149.8 tok/s decode · 1.3s TTFT (warm) · 375 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 149.8 tok/s | 705 ms | 1.5M |
| Coding | C | Runs well | 149.8 tok/s | 1292 ms | 1.5M |
| Agentic Coding | C | Runs well | 149.8 tok/s | 1880 ms | 1.5M |
| Reasoning | C | Runs well | 149.8 tok/s | 1527 ms | 1.5M |
| RAG | C | Runs well | 149.8 tok/s | 2350 ms | 1.5M |
Inference speed
SOLAR 10.7B Instruct v1.0 uncensored inference speed — tokens per second by GPU & Mac
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.
Quantization options
How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | D38 |
Q3_K_S | 3 | 5.2 GB | Low | D38 |
NVFP4 | 4 | 6.0 GB | Medium | D38 |
Q4_K_M | 4 | 6.5 GB | Medium | D38 |
Q5_K_M | 5 | 7.7 GB | High | D38 |
Q6_K | 6 | 8.8 GB | High | D38 |
Q8_0 | 8 | 11.4 GB | Very High | D38 |
F16Best for your GPU | 16 | 21.9 GB | Maximum | D39 |
Get started
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 startUpgrade-Optionen
Hardware, die SOLAR 10.7B Instruct v1.0 uncensored gut ausführt
Frequently asked questions
Can NVIDIA H200 PCIe 141GB run SOLAR 10.7B Instruct v1.0 uncensored?
Yes, NVIDIA H200 PCIe 141GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 149.8 tok/s.
How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?
SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 23.1 GB of memory with Q4_K_M quantization.
What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?
The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.
What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 149.8 tokens per second decode speed with a time-to-first-token of 1292ms using Q4_K_M quantization.
Can NVIDIA H200 PCIe 141GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?
For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on NVIDIA H200 PCIe 141GB receives a C grade with 149.8 tok/s and 1.5M context.
What context window can SOLAR 10.7B Instruct v1.0 uncensored use on NVIDIA H200 PCIe 141GB?
On NVIDIA H200 PCIe 141GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 1.5M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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