NVIDIA H20 96GBBudget pick96 GB VRAM (+16)4000 GB/s (+650)
C
Raises estimated decode speed by about 39%.53.1 tok/s decode
Raises estimated decode speed by about 39%.
~$12,000 MSRP
Solar Open 100B needs ~81.6 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~38 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~1.2 GB host RAM)
Decode
38.1 tok/s
TTFT
5086 ms
Safe context
14K
Memory
81.6 GB / 80.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 46.1 tok/s | 2289 ms | 14K |
| Coding | C | Runs with offload (needs ~1.2 GB host RAM) | 38.1 tok/s | 5086 ms | 14K |
| Agentic Coding | C | Very compromised (needs ~8.7 GB host RAM) | 30.5 tok/s | 9230 ms | 14K |
| Reasoning | C | Runs with offload (needs ~1.2 GB host RAM) | 38.1 tok/s | 6010 ms | 14K |
| RAG | C | Very compromised (needs ~8.7 GB host RAM) | 30.5 tok/s |
How Solar Open 100B (100B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | C47 |
Q3_K_S | 3 | 49.0 GB | Low | C48 |
NVFP4 | 4 |
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startUpgrade options
NVIDIA H20 96GBBudget pickRaises estimated decode speed by about 39%.
~$12,000 MSRP
NVIDIA H200 141GBBest valueRaises estimated decode speed by about 73%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBNVIDIA upgradeRaises estimated decode speed by about 73%.
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Yes, NVIDIA H100 80GB can run Solar Open 100B with a C grade (Runs with offload (needs ~1.2 GB host RAM)). Expected decode speed: 38.1 tok/s.
Solar Open 100B (100B parameters) requires approximately 81.6 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 NVIDIA H100 80GB, Solar Open 100B achieves approximately 38.1 tokens per second decode speed with a time-to-first-token of 5086ms using Q4_K_M quantization.
For coding workloads, Solar Open 100B on NVIDIA H100 80GB receives a C grade with 38.1 tok/s and 14K context.
On NVIDIA H100 80GB, Solar Open 100B can safely use up to 14K 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.
<iframe src="https://willitrunai.com/embed/hf-aaryank--solar-open-100b-gguf-on-h100-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 11538 ms |
| 14K |
| Medium |
| C48 |
Q4_K_MBest for your GPU | 4 | 61.0 GB | Medium | C48 |
Q5_K_M | 5 | 72.0 GB | High | F0 |
Q6_K | 6 | 82.0 GB | High | F0 |
Q8_0 | 8 | 107.0 GB | Very High | F0 |
F16 | 16 | 205.0 GB | Maximum | F0 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.