Can Solar Open 2 250B run on NVIDIA H200 141GB?

BARELY — Tight on Memory

A74Great

Estimated from fit model

Solar Open 2 250B needs ~168.4 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 168.4 GB, 52.5 tok/s, Very compromised (needs ~24.9 GB host RAM)

168.4 GB required141.0 GB available

119% VRAM needed

27.4 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~24.9 GB host RAM)

Decode

52.5 tok/s

TTFT

3687 ms

Safe context

Memory

168.4 GB / 141.0 GB

Offload

20%

Memory breakdown

Weights152.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsSolar Open 2 250B on NVIDIA H200 141GB
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: 52.5 tok/s decode · 3.7s TTFT (warm) · 131 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 24.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~24.6 GB host RAM)	52.7 tok/s	2004 ms	4K
Coding	A	Very compromised (needs ~24.9 GB host RAM)	52.5 tok/s	3687 ms	4K
Agentic Coding	A	Very compromised (needs ~25.4 GB host RAM)	52.1 tok/s	5401 ms	4K
Reasoning	A	Very compromised (needs ~24.9 GB host RAM)	52.5 tok/s	4357 ms	4K
RAG	A	Very compromised (needs ~25.4 GB host RAM)	52.1 tok/s	6751 ms	4K

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 H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	36.0 GB	Very Low	A81
Q2_0_G128	1.71	66.8 GB	Low	S85
Q2_KBest for your GPU	2	97.6 GB	Low	S86
Q3_K_S	3	122.6 GB	Low	F0
NVFP4	4	140.2 GB	Medium	F0
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

Frequently asked questions

Can NVIDIA H200 141GB run Solar Open 2 250B?

Yes, NVIDIA H200 141GB can run Solar Open 2 250B with a A grade (Very compromised (needs ~24.9 GB host RAM)). Expected decode speed: 52.5 tok/s.

How much VRAM does Solar Open 2 250B need?

Solar Open 2 250B (250.3000030517578B parameters) requires approximately 168.4 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 H200 141GB?

On NVIDIA H200 141GB, Solar Open 2 250B achieves approximately 52.5 tokens per second decode speed with a time-to-first-token of 3687ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Solar Open 2 250B for coding?

For coding workloads, Solar Open 2 250B on NVIDIA H200 141GB receives a A grade with 52.5 tok/s and 4K context.

What context window can Solar Open 2 250B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Solar Open 2 250B can safely use up to 4K 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 H200 141GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for NVIDIA H200 141GB See all hardware for Solar Open 2 250B

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