AMD Instinct MI350X 288GBBudget pick
288 GB VRAM (+32)8000 GB/s (+2000)
C
Raises estimated decode speed by about 33%.95.7 tok/s decode
Raises estimated decode speed by about 33%.
~$8,000 MSRP
Can Solar Open 100B run on AMD Instinct MI325X 256GB?
YES — Runs Great
C49Usable
○Estimated from fit model
Solar Open 100B needs ~99.2 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~72 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 99.2 GB, 71.8 tok/s, Runs well
99.2 GB required256.0 GB available
39% VRAM used
Fit status
Runs well
Decode
71.8 tok/s
TTFT
2696 ms
Safe context
230K
Memory
99.2 GB / 256.0 GB
Memory breakdown
Weights61.0 GB
KV Cache11.7 GB
Runtime0.9 GB
Headroom25.6 GB
See how fast it feels
See how fast it feelsSolar Open 100B on AMD Instinct MI325X 256GB
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: 71.8 tok/s decode · 2.7s TTFT (warm) · 180 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 | 71.8 tok/s | 1471 ms | 230K |
| Coding | C | Runs well | 71.8 tok/s | 2696 ms | 230K |
| Agentic Coding | C | Runs well | 71.8 tok/s | 3922 ms | 230K |
| Reasoning | C | Runs well | 71.8 tok/s | 3186 ms | 230K |
| RAG | C | Runs well | 71.8 tok/s | 4902 ms | 230K |
Quantization options
How Solar Open 100B (100B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 39.0 GB | Low | D39 |
Q3_K_S | 3 | 49.0 GB | Low | D39 |
NVFP4 | 4 | 56.0 GB | Medium | C40 |
Q4_K_M | 4 | 61.0 GB | Medium | C40 |
Q5_K_M | 5 | 72.0 GB | High | C41 |
Q6_K | 6 | 82.0 GB | High | C42 |
Q8_0 | 8 | 107.0 GB | Very High | C44 |
F16Best for your GPU | 16 | 205.0 GB | Maximum | C48 |
Get started
Copy-paste commands to run Solar Open 100B on your machine.
Run
lms load hf-aaryank--solar-open-100b-gguf && lms server startUpgrade options