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URL: https://willitrunai.com/can-run/hf-mradermacher--solar-open-69b-reap-i1-gguf-on-instinct-mi350x-288gb

⇱ Solar Open 69B REAP i1 on AMD Instinct MI350X 288GB? YES

Can Solar Open 69B REAP i1 run on AMD Instinct MI350X 288GB?

YES — Runs Great

C48Usable
○Estimated from fit model

Solar Open 69B REAP i1 needs ~79.9 GB VRAM. AMD Instinct MI350X 288GB has 288.0 GB. With Q4_K_M quantization, expect ~139 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 79.9 GB, 138.8 tok/s, Runs well
79.9 GB required288.0 GB available
28% VRAM used

Fit status

Runs well

Decode

138.8 tok/s

TTFT

1395 ms

Safe context

428K

Memory

79.9 GB / 288.0 GB

Memory breakdown

Weights42.1 GB
KV Cache8.1 GB
Runtime0.9 GB
Headroom28.8 GB

See how fast it feels

See how fast it feelsSolar Open 69B REAP i1 on AMD Instinct MI350X 288GB
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: 138.8 tok/s decode · 1.4s TTFT (warm) · 347 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well138.8 tok/s761 ms428K
CodingCRuns well138.8 tok/s1395 ms428K
Agentic CodingCRuns well138.8 tok/s2030 ms428K
ReasoningCRuns well138.8 tok/s1649 ms428K
RAGCRuns well138.8 tok/s2537 ms428K

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on AMD Instinct MI350X 288GB (288.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
26.9 GB
LowD37
Q3_K_S
3
33.8 GB
LowD38
NVFP4
4
38.6 GB
MediumD38
Q4_K_M
4
42.1 GB
MediumD38
Q5_K_M
5
49.7 GB
HighD39
Q6_K
6
56.6 GB
HighD39
Q8_0
8
73.8 GB
Very HighC41
F16Best for your GPU
16
141.5 GB
MaximumC46

Get started

Copy-paste commands to run Solar Open 69B REAP i1 on your machine.

Run

lms load hf-mradermacher--solar-open-69b-reap-i1-gguf && lms server start

Frequently asked questions

See all results for AMD Instinct MI350X 288GBSee all hardware for Solar Open 69B REAP i1