Solar Open 100B needs ~75.0 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With NVFP4 quantization, expect ~11 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
16.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.6 tok/s
TTFT
22624 ms
Safe context
4K
Memory
80.0 GB / 64.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 8.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~8.4 GB host RAM) | 10.0 tok/s | 10515 ms | 4K |
| Coding | F | Too heavy | 8.6 tok/s | 22624 ms | 4K |
| Agentic Coding | F | Too heavy | 6.4 tok/s | 43876 ms | 4K |
| Reasoning | F | Too heavy | 8.6 tok/s | 26737 ms | 4K |
| RAG | F | Too heavy | 6.4 tok/s | 54845 ms | 4K |
How Solar Open 100B (100B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | C48 |
Q3_K_SBest for your GPU | 3 | 49.0 GB | Low | C48 |
NVFP4 | 4 | 56.0 GB | Medium | F0 |
Q4_K_M | 4 | 61.0 GB | Medium | F0 |
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 |
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startUpgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$8,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$12,000 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$15,000 MSRP
Yes, AMD Instinct MI210 64GB can run Solar Open 100B at NVFP4 quantization (Very compromised (needs ~8.2 GB host RAM)). The recommended Q4_K_M requires 80.0 GB which exceeds available memory, but at NVFP4 it needs only 75.0 GB. Expected decode speed: 11.2 tok/s.
Solar Open 100B (100B parameters) requires approximately 80.0 GB at Q4_K_M quantization. On AMD Instinct MI210 64GB, it fits at NVFP4 using 75.0 GB.
The recommended quantization is Q4_K_M, but on AMD Instinct MI210 64GB the best fitting quantization is NVFP4, which uses 75.0 GB.
On AMD Instinct MI210 64GB, Solar Open 100B achieves approximately 11.2 tokens per second decode speed with a time-to-first-token of 17268ms using NVFP4 quantization.
For coding workloads, Solar Open 100B on AMD Instinct MI210 64GB receives a F grade with 8.6 tok/s and 4K context.
On AMD Instinct MI210 64GB, Solar Open 100B can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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