NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+1600)
C
Raises estimated decode speed by about 62%.66.1 tok/sデコード
Raises estimated decode speed by about 62%.
〜$30,000 MSRP
Can Solar Open 100B run on AMD Instinct MI250X 128GB?
YES — Runs Great
C54Usable
Estimated from fit model
Solar Open 100B needs ~86.4 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 86.4 GB, 40.9 tok/s, Runs well
86.4 GB required128.0 GB available
68% VRAM used
Fit status
Runs well
Decode
40.9 tok/s
TTFT
4731 ms
Safe context
73K
Memory
86.4 GB / 128.0 GB
Memory breakdown
Weights61.0 GB
KV Cache11.7 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsSolar Open 100B on AMD Instinct MI250X 128GB
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: 40.9 tok/s decode · 4.7s TTFT (warm) · 102 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 | 40.9 tok/s | 2581 ms | 73K |
| Coding | C | Runs well | 40.9 tok/s | 4731 ms | 73K |
| Agentic Coding | C | Runs well | 40.9 tok/s | 6882 ms | 73K |
| Reasoning | C | Runs well | 40.9 tok/s | 5592 ms | 73K |
| RAG | C | Runs well | 40.9 tok/s | 8603 ms | 73K |
Inference speed
Solar Open 100B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Solar Open 100B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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? |
|---|---|---|---|---|
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 9.8 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 9.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 7.6 | Tight |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 7.2 | Tight |
2× RX 7900 XTX 24GB | 48 GB | Q4_K_M | 5.1 | Too big |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 4.9 | Too big |
| RTX 5090 32GB | 32 GB | Q4_K_M | 3.0 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 2.8 | Too big |
| 2× RTX 4090 24GB | 48 GB | Q4_K_M | 2.7 | Too big |
| 2× RTX 3090 24GB | 48 GB | Q4_K_M | 2.5 | Too big |
| 4× RTX 3060 12GB | 48 GB | Q4_K_M | 2.2 | Too big |
| RTX 4090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 2.0 | Too big |
| RTX 3090 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 2.0 | Too big |
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 Open 100B (100B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | C42 |
Q3_K_S | 3 | 49.0 GB | Low | C44 |
NVFP4 | 4 | 56.0 GB | Medium | C45 |
Q4_K_M | 4 | 61.0 GB | Medium | C46 |
Q5_K_M | 5 | 72.0 GB | High | C48 |
Q6_K | 6 | 82.0 GB | High | C48 |
Q8_0Best for your GPU | 8 | 107.0 GB | Very High | C48 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Get started
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startアップグレードオプション
Solar Open 100Bを快適に動かすハードウェア
Frequently asked questions
Can AMD Instinct MI250X 128GB run Solar Open 100B?
Yes, AMD Instinct MI250X 128GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 40.9 tok/s.
How much VRAM does Solar Open 100B need?
Solar Open 100B (100B parameters) requires approximately 86.4 GB of memory with Q4_K_M quantization.
What is the best quantization for Solar Open 100B?
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
What speed will Solar Open 100B run at on AMD Instinct MI250X 128GB?
On AMD Instinct MI250X 128GB, Solar Open 100B achieves approximately 40.9 tokens per second decode speed with a time-to-first-token of 4731ms using Q4_K_M quantization.
Can AMD Instinct MI250X 128GB run Solar Open 100B for coding?
For coding workloads, Solar Open 100B on AMD Instinct MI250X 128GB receives a C grade with 40.9 tok/s and 73K context.
What context window can Solar Open 100B use on AMD Instinct MI250X 128GB?
On AMD Instinct MI250X 128GB, Solar Open 100B can safely use up to 73K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-aaryank--solar-open-100b-gguf-on-instinct-mi250x-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: