Can Solar Open 100B run on AMD Instinct MI250 128GB?

YES — Runs Great

C53Usable

○Estimated from fit model

Solar Open 100B needs ~86.4 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~36 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 86.4 GB, 35.7 tok/s, Runs well

86.4 GB required128.0 GB available

68% VRAM used

Fit status

Runs well

Decode

35.7 tok/s

TTFT

5427 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 MI250 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: 35.7 tok/s decode · 5.4s TTFT (warm) · 89 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	35.7 tok/s	2960 ms	73K
Coding	C	Runs well	35.7 tok/s	5427 ms	73K
Agentic Coding	C	Runs well	35.7 tok/s	7894 ms	73K
Reasoning	C	Runs well	35.7 tok/s	6414 ms	73K
RAG	C	Runs well	35.7 tok/s	9868 ms	73K

Quantization options

How Solar Open 100B (100B params) fits at each quantization level on AMD Instinct MI250 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

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

Upgrade options

Hardware that runs Solar Open 100B well

👁 NVIDIA
NVIDIA H200 141GBBudget pick

141 GB VRAM (+13)4800 GB/s (+1600)

Raises estimated decode speed by about 85%.66.1 tok/s decode

Raises estimated decode speed by about 85%.

~$30,000 MSRP

👁 NVIDIA
NVIDIA H200 PCIe 141GBBest value