Can Solar Open 100B run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Tight Fit

C49Usable

Estimated from fit model

Solar Open 100B needs ~83.5 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 83.5 GB, 24.7 tok/s, Tight fit

83.5 GB required96.0 GB available

87% VRAM used

Fit status

Tight fit

Decode

24.7 tok/s

TTFT

7845 ms

Safe context

33K

Memory

83.5 GB / 96.0 GB

Memory breakdown

Weights61.0 GB

KV Cache11.7 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsSolar Open 100B on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 24.7 tok/s decode · 7.8s TTFT (warm) · 62 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	24.7 tok/s	4279 ms	33K
Coding	C	Tight fit	24.7 tok/s	7845 ms	33K
Agentic Coding	C	Runs with offload	24.7 tok/s	11412 ms	33K
Reasoning	C	Tight fit	24.7 tok/s	9272 ms	33K
RAG	C	Runs with offload	24.7 tok/s	14264 ms	33K

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 RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	39.0 GB	Low	C45
Q3_K_S	3	49.0 GB	Low	C47
NVFP4	4	56.0 GB	Medium	C48
Q4_K_M	4	61.0 GB	Medium	C48
Q5_K_MBest for your GPU	5	72.0 GB	High	C48
Q6_K	6	82.0 GB	High	F0
Q8_0	8	107.0 GB	Very High	F0
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

Opciones de mejora

Hardware que ejecuta bien Solar Open 100B

NVIDIA H200 141GBOpción económica

141 GB VRAM (+45)4800 GB/s (+3008)

Sube la velocidad estimada de decodificación alrededor de un 168%.66.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 168%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

141 GB VRAM (+45)4800 GB/s (+3008)

Sube la velocidad estimada de decodificación alrededor de un 168%.66.1 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 168%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA B200 180GBMejora NVIDIA

180 GB VRAM (+84)8000 GB/s (+6208)

Sube la velocidad estimada de decodificación alrededor de un 346%.110.2 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 346%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Solar Open 100B?

Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Solar Open 100B with a C grade (Tight fit). Expected decode speed: 24.7 tok/s.

How much VRAM does Solar Open 100B need?

Solar Open 100B (100B parameters) requires approximately 83.5 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 RTX PRO 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, Solar Open 100B achieves approximately 24.7 tokens per second decode speed with a time-to-first-token of 7845ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Solar Open 100B for coding?

For coding workloads, Solar Open 100B on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a C grade with 24.7 tok/s and 33K context.

What context window can Solar Open 100B use on RTX PRO 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, Solar Open 100B can safely use up to 33K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 6000 Blackwell Workstation Edition 96GB See all hardware for Solar Open 100B

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