Can Solar Open 100B run on NVIDIA H200 141GB?

YES — Runs Great

C54Usable

Estimated from fit model

Solar Open 100B needs ~88.0 GB VRAM. NVIDIA H200 141GB has 141.0 GB. With Q4_K_M quantization, expect ~66 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: 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) — 88.0 GB, 66.1 tok/s, Runs well

88.0 GB required141.0 GB available

62% VRAM used

Fit status

Runs well

Decode

66.1 tok/s

TTFT

2929 ms

Safe context

88K

Memory

88.0 GB / 141.0 GB

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime1.2 GB

Headroom14.1 GB

See how fast it feels

See how fast it feelsSolar Open 100B on NVIDIA H200 141GB
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: 66.1 tok/s decode · 2.9s TTFT (warm) · 165 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	66.1 tok/s	1598 ms	88K
Coding	C	Runs well	66.1 tok/s	2929 ms	88K
Agentic Coding	B	Runs well	66.1 tok/s	4260 ms	88K
Reasoning	C	Runs well	66.1 tok/s	3462 ms	88K
RAG	B	Runs well	66.1 tok/s	5325 ms	88K

Inference speed

Solar Open 100B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Solar Open 100B at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~10 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?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	9.8	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	9.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	7.6	Tight
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	7.2	Tight
2× RX 7900 XTX 24GB	48 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	3.0	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.8	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.7	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.5	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.2	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	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
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 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 100B (100B params) fits at each quantization level on NVIDIA H200 141GB (141.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	C42
Q3_K_S	3	49.0 GB	Low	C43
NVFP4	4	56.0 GB	Medium	C44
Q4_K_M	4	61.0 GB	Medium	C45
Q5_K_M	5	72.0 GB	High	C47
Q6_K	6	82.0 GB	High	C48
Q8_0Best for your GPU	8	107.0 GB	Very High	C48
F16	16	205.0 GB	Maximum	F0

Get started

Copy-paste commands to run Solar Open 100B on your machine.

Run

lms load hf-aaryank--solar-open-100b-gguf && lms server start

Frequently asked questions

Can NVIDIA H200 141GB run Solar Open 100B?

Yes, NVIDIA H200 141GB can run Solar Open 100B with a C grade (Runs well). Expected decode speed: 66.1 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 88.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Solar Open 100B?

The recommended quantization for Solar Open 100B is Q4_K_M, which balances quality and memory efficiency.

What speed will Solar Open 100B run at on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Solar Open 100B achieves approximately 66.1 tokens per second decode speed with a time-to-first-token of 2929ms using Q4_K_M quantization.

Can NVIDIA H200 141GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on NVIDIA H200 141GB receives a C grade with 66.1 tok/s and 88K context.

What context window can Solar Open 100B use on NVIDIA H200 141GB?

On NVIDIA H200 141GB, Solar Open 100B can safely use up to 88K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA H200 141GB See all hardware for Solar Open 100B

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