Solar Open 100B needs ~92.8 GB VRAM. AMD Instinct MI300X 192GB has 192.0 GB. With Q4_K_M quantization, expect ~68 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
67.8 tok/s
TTFT
2857 ms
Safe context
151K
Memory
92.8 GB / 192.0 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 | 67.8 tok/s | 1558 ms | 151K |
| Coding | C | Runs well | 67.8 tok/s | 2857 ms | 151K |
| Agentic Coding | C | Runs well | 67.8 tok/s | 4155 ms | 151K |
| Reasoning | C | Runs well | 67.8 tok/s | 3376 ms | 151K |
| RAG | C | Runs well | 67.8 tok/s | 5194 ms | 151K |
Inference speed
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.
How Solar Open 100B (100B params) fits at each quantization level on AMD Instinct MI300X 192GB (192.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | D40 |
Q3_K_S | 3 | 49.0 GB | Low | C41 |
NVFP4 | 4 | 56.0 GB | Medium | C42 |
Q4_K_M | 4 | 61.0 GB | Medium | C42 |
Q5_K_M | 5 | 72.0 GB | High | C44 |
Q6_K | 6 | 82.0 GB | High | C45 |
Q8_0Best for your GPU | 8 | 107.0 GB | Very High | C47 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startYes, AMD Instinct MI300X 192GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 67.8 tok/s.
Solar Open 100B (100B parameters) requires approximately 92.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI300X 192GB, Solar Open 100B achieves approximately 67.8 tokens per second decode speed with a time-to-first-token of 2857ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on AMD Instinct MI300X 192GB receives a C grade with 67.8 tok/s and 151K context.
On AMD Instinct MI300X 192GB, Solar Open 100B can safely use up to 151K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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