Can Solar Open 2 250B run on AMD Instinct MI325X 256GB?

YES — Runs Great

S94Excellent

Estimated from fit model

Solar Open 2 250B needs ~182.1 GB VRAM. AMD Instinct MI325X 256GB has 256.0 GB. With Q4_K_M quantization, expect ~82 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) — 179.9 GB, 89.7 tok/s, Runs well

179.9 GB required256.0 GB available

70% VRAM used

Fit status

Runs well

Decode

89.7 tok/s

TTFT

2159 ms

Safe context

1.0M

Memory

179.9 GB / 256.0 GB

Memory breakdown

Weights152.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom25.6 GB

See how fast it feels

See how fast it feelsSolar Open 2 250B on AMD Instinct MI325X 256GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 89.7 tok/s decode · 2.2s TTFT (warm) · 224 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	S	Runs well	82.4 tok/s	1281 ms	420K
Coding	S	Runs well	82.4 tok/s	2348 ms	420K
Agentic Coding	S	Runs well	82.4 tok/s	3416 ms	420K
Reasoning	S	Runs well	82.4 tok/s	2775 ms	420K
RAG	S	Runs well	82.4 tok/s	4269 ms	420K

Inference speed

Solar Open 2 250B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 2 250B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~18 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?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	17.7	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.9	Too big
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	6.6	Too big
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	5.1	Too big
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	4.9	Too big
RTX 5090 32GB	32 GB	Q4_K_M	3.7	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.6	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	3.4	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	3.0	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.5	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.4	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.1	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.1	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	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
4× RTX 3060 12GB	48 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 2 250B (250.3000030517578B params) fits at each quantization level on AMD Instinct MI325X 256GB (256.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	36.0 GB	Very Low	A77
Q2_0_G128	1.71	66.8 GB	Low	A80
Q2_K	2	97.6 GB	Low	A82
Q3_K_S	3	122.6 GB	Low	A84
NVFP4	4	140.2 GB	Medium	S86
Q4_K_M	4	152.7 GB	Medium	S86
Q5_K_M	5	180.2 GB	High	S86
Q6_KBest for your GPU	6	205.2 GB	High	S86
Q8_0	8	267.8 GB	Very High	F0
F16	16	513.1 GB	Maximum	F0

Get started

Copy-paste commands to run Solar Open 2 250B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "upstage/Solar-Open2-250B" \
  --hf-file "Solar-Open2-250B-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

More models your AMD Instinct MI325X 256GB can run

Model	Params	Grade	Decode
DeepSeek V4 Flash	284B	S	94.4 tok/s
Qwen 3.5 397B A17B	397B	A	39.2 tok/s
Hy3	295B	S	74.2 tok/s

Frequently asked questions

Can AMD Instinct MI325X 256GB run Solar Open 2 250B?

Yes, AMD Instinct MI325X 256GB can run Solar Open 2 250B with a S grade (Runs well). Expected decode speed: 82.4 tok/s.

How much VRAM does Solar Open 2 250B need?

Solar Open 2 250B (250.3000030517578B parameters) requires approximately 182.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 2 250B?

The recommended quantization for Solar Open 2 250B is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 2 250B run at on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Solar Open 2 250B achieves approximately 82.4 tokens per second decode speed with a time-to-first-token of 2348ms using Q4_K_M quantization.

Can AMD Instinct MI325X 256GB run Solar Open 2 250B for coding?

For coding workloads, Solar Open 2 250B on AMD Instinct MI325X 256GB receives a S grade with 82.4 tok/s and 420K context.

What context window can Solar Open 2 250B use on AMD Instinct MI325X 256GB?

On AMD Instinct MI325X 256GB, Solar Open 2 250B can safely use up to 420K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

See all results for AMD Instinct MI325X 256GB See all hardware for Solar Open 2 250B

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