Can OpenChat 3.5 7B Qwen v2.0 i1 run on RTX PRO 5000 Blackwell 48GB?

YES — Runs Great

C46Usable

Estimated from fit model

OpenChat 3.5 7B Qwen v2.0 i1 needs ~11.1 GB VRAM. RTX PRO 5000 Blackwell 48GB has 48.0 GB. With Q4_K_M quantization, expect ~98 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) — 11.1 GB, 98.0 tok/s, Runs well

11.1 GB required48.0 GB available

23% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

736K

Memory

11.1 GB / 48.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsOpenChat 3.5 7B Qwen v2.0 i1 on RTX PRO 5000 Blackwell 48GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	98.0 tok/s	1078 ms	736K
Coding	C	Runs well	98.0 tok/s	1976 ms	736K
Agentic Coding	C	Runs well	98.0 tok/s	2873 ms	736K
Reasoning	C	Runs well	98.0 tok/s	2335 ms	736K
RAG	C	Runs well	98.0 tok/s	3592 ms	736K

Quantization options

How OpenChat 3.5 7B Qwen v2.0 i1 (7B params) fits at each quantization level on RTX PRO 5000 Blackwell 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C41
Q3_K_S	3	3.4 GB	Low	C41
NVFP4	4	3.9 GB	Medium	C41
Q4_K_M	4	4.3 GB	Medium	C41
Q5_K_M	5	5.0 GB	High	C41
Q6_K	6	5.7 GB	High	C41
Q8_0	8	7.5 GB	Very High	C42
F16Best for your GPU	16	14.3 GB	Maximum	C44

Get started

Copy-paste commands to run OpenChat 3.5 7B Qwen v2.0 i1 on your machine.

Run

lms load hf-mradermacher--openchat-3-5-7b-qwen-v2-0-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien OpenChat 3.5 7B Qwen v2.0 i1

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.87.8 tok/s decodificación

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBMejor relación calidad-precio

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.98 tok/s decodificación

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

~$3,999 MSRP

Frequently asked questions

Can RTX PRO 5000 Blackwell 48GB run OpenChat 3.5 7B Qwen v2.0 i1?

Yes, RTX PRO 5000 Blackwell 48GB can run OpenChat 3.5 7B Qwen v2.0 i1 with a C grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does OpenChat 3.5 7B Qwen v2.0 i1 need?

OpenChat 3.5 7B Qwen v2.0 i1 (7B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.

What is the best quantization for OpenChat 3.5 7B Qwen v2.0 i1?

The recommended quantization for OpenChat 3.5 7B Qwen v2.0 i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will OpenChat 3.5 7B Qwen v2.0 i1 run at on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, OpenChat 3.5 7B Qwen v2.0 i1 achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RTX PRO 5000 Blackwell 48GB run OpenChat 3.5 7B Qwen v2.0 i1 for coding?

For coding workloads, OpenChat 3.5 7B Qwen v2.0 i1 on RTX PRO 5000 Blackwell 48GB receives a C grade with 98.0 tok/s and 736K context.

What context window can OpenChat 3.5 7B Qwen v2.0 i1 use on RTX PRO 5000 Blackwell 48GB?

On RTX PRO 5000 Blackwell 48GB, OpenChat 3.5 7B Qwen v2.0 i1 can safely use up to 736K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX PRO 5000 Blackwell 48GB See all hardware for OpenChat 3.5 7B Qwen v2.0 i1

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