Can Solar Open 2 250B run on NVIDIA B200 180GB?

YES — With Offload

S92Excellent

Estimated from fit model

Solar Open 2 250B needs ~172.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~138 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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) — 172.3 GB, 137.6 tok/s, Runs with offload

172.3 GB required180.0 GB available

96% VRAM used

Fit status

Runs with offload

Decode

137.6 tok/s

TTFT

1407 ms

Safe context

184K

Memory

172.3 GB / 180.0 GB

Memory breakdown

Weights152.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom18.0 GB

See how fast it feels

See how fast it feelsSolar Open 2 250B on NVIDIA B200 180GB
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: 137.6 tok/s decode · 1.4s TTFT (warm) · 344 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs with offload	137.6 tok/s	768 ms	184K
Coding	S	Runs with offload	137.6 tok/s	1407 ms	184K
Agentic Coding	S	Runs with offload	137.6 tok/s	2047 ms	184K
Reasoning	S	Runs with offload	137.6 tok/s	1663 ms	184K
RAG	S	Runs with offload	137.6 tok/s	2559 ms	184K

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 NVIDIA B200 180GB (180.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	36.0 GB	Very Low	A79
Q2_0_G128	1.71	66.8 GB	Low	A83
Q2_K	2	97.6 GB	Low	S86
Q3_K_S	3	122.6 GB	Low	S86
NVFP4Best for your GPU	4	140.2 GB	Medium	S86
Q4_K_M	4	152.7 GB	Medium	F0
Q5_K_M	5	180.2 GB	High	F0
Q6_K	6	205.2 GB	High	F0
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 NVIDIA B200 180GB can run

Model	Params	Grade	Decode	Capabilities
DeepSeek V4 Flash	284B	S	144.8 tok/s
Hy3	295B	A	79.1 tok/s

Frequently asked questions

Can NVIDIA B200 180GB run Solar Open 2 250B?

Yes, NVIDIA B200 180GB can run Solar Open 2 250B with a S grade (Runs with offload). Expected decode speed: 137.6 tok/s.

How much VRAM does Solar Open 2 250B need?

Solar Open 2 250B (250.3000030517578B parameters) requires approximately 172.3 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 NVIDIA B200 180GB?

On NVIDIA B200 180GB, Solar Open 2 250B achieves approximately 137.6 tokens per second decode speed with a time-to-first-token of 1407ms using Q4_K_M quantization.

Can NVIDIA B200 180GB run Solar Open 2 250B for coding?

For coding workloads, Solar Open 2 250B on NVIDIA B200 180GB receives a S grade with 137.6 tok/s and 184K context.

What context window can Solar Open 2 250B use on NVIDIA B200 180GB?

On NVIDIA B200 180GB, Solar Open 2 250B can safely use up to 184K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.

What should I upgrade first if Solar Open 2 250B feels slow on NVIDIA B200 180GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA B200 180GB See all hardware for Solar Open 2 250B

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