Can Solar Open 69B REAP i1 run on AMD Instinct MI250X 128GB?

YES — Runs Great

C51Usable

Estimated from fit model

Solar Open 69B REAP i1 needs ~63.9 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q4_K_M quantization, expect ~59 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) — 63.9 GB, 59.3 tok/s, Runs well

63.9 GB required128.0 GB available

50% VRAM used

Fit status

Runs well

Decode

59.3 tok/s

TTFT

3265 ms

Safe context

143K

Memory

63.9 GB / 128.0 GB

Memory breakdown

Weights42.1 GB

KV Cache8.1 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsSolar Open 69B REAP i1 on AMD Instinct MI250X 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: 59.3 tok/s decode · 3.3s TTFT (warm) · 148 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	59.3 tok/s	1781 ms	143K
Coding	C	Runs well	59.3 tok/s	3265 ms	143K
Agentic Coding	C	Runs well	59.3 tok/s	4749 ms	143K
Reasoning	C	Runs well	59.3 tok/s	3858 ms	143K
RAG	C	Runs well	59.3 tok/s	5936 ms	143K

Inference speed

Solar Open 69B REAP i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 69B REAP i1 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	15.2	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	11.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.5	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	10.2	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	8.0	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.3	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.3	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.5	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.8	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.1	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.8	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.5	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.2	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Solar Open 69B REAP i1 (69B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	26.9 GB	Low	C40
Q3_K_S	3	33.8 GB	Low	C41
NVFP4	4	38.6 GB	Medium	C42
Q4_K_M	4	42.1 GB	Medium	C43
Q5_K_M	5	49.7 GB	High	C44
Q6_K	6	56.6 GB	High	C45
Q8_0Best for your GPU	8	73.8 GB	Very High	C48
F16	16	141.5 GB	Maximum	F0

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

アップグレードオプション

Solar Open 69B REAP i1を快適に動かすハードウェア

NVIDIA H200 141GBコスパ重視

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

Raises estimated decode speed by about 62%.95.8 tok/sデコード

Raises estimated decode speed by about 62%.

〜$30,000 MSRP

NVIDIA H200 PCIe 141GBコスパ最良

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

Raises estimated decode speed by about 62%.95.8 tok/sデコード

Raises estimated decode speed by about 62%.

〜$30,000 MSRP

Frequently asked questions

Can AMD Instinct MI250X 128GB run Solar Open 69B REAP i1?

Yes, AMD Instinct MI250X 128GB can run Solar Open 69B REAP i1 with a C grade (Runs well). Expected decode speed: 59.3 tok/s.

How much VRAM does Solar Open 69B REAP i1 need?

Solar Open 69B REAP i1 (69B parameters) requires approximately 63.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 69B REAP i1?

The recommended quantization for Solar Open 69B REAP i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 69B REAP i1 run at on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, Solar Open 69B REAP i1 achieves approximately 59.3 tokens per second decode speed with a time-to-first-token of 3265ms using Q4_K_M quantization.

Can AMD Instinct MI250X 128GB run Solar Open 69B REAP i1 for coding?

For coding workloads, Solar Open 69B REAP i1 on AMD Instinct MI250X 128GB receives a C grade with 59.3 tok/s and 143K context.

What context window can Solar Open 69B REAP i1 use on AMD Instinct MI250X 128GB?

On AMD Instinct MI250X 128GB, Solar Open 69B REAP i1 can safely use up to 143K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for AMD Instinct MI250X 128GB See all hardware for Solar Open 69B REAP i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-open-69b-reap-i1-gguf-on-instinct-mi250x-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: