Can jointpreferences mistral 7b sft helpful run on Radeon PRO W7600 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

jointpreferences mistral 7b sft helpful needs ~6.8 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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) — 6.8 GB, 39.8 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

39.8 tok/s

TTFT

4865 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsjointpreferences   mistral 7b sft helpful on Radeon PRO W7600 8GB
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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 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	39.8 tok/s	2654 ms	40K
Coding	C	Tight fit	39.8 tok/s	4865 ms	40K
Agentic Coding	C	Runs with offload	39.8 tok/s	7076 ms	40K
Reasoning	C	Tight fit	39.8 tok/s	5750 ms	40K
RAG	C	Runs with offload	39.8 tok/s	8846 ms	40K

Quantization options

How jointpreferences mistral 7b sft helpful (7B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run jointpreferences mistral 7b sft helpful on your machine.

Run

lms load hf-richarderkhov--jointpreferences---mistral-7b-sft-helpful-gguf && lms server start

Upgrade options

Hardware that runs jointpreferences mistral 7b sft helpful well

RX 7700 XT 12GBBudget pick

12 GB VRAM (+4)432 GB/s (+144)

Raises estimated decode speed by about 53%.60.7 tok/s decode

Raises estimated decode speed by about 53%.

Adds memory headroom for longer context windows and future model growth.

~$449 MSRP

RX 6700 XT 12GBBest value

12 GB VRAM (+4)384 GB/s (+96)

Adds memory headroom for longer context windows and future model growth.46.8 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+8)640 GB/s (+352)

Raises estimated decode speed by about 133%.92.9 tok/s decode

Raises estimated decode speed by about 133%.

Adds memory headroom for longer context windows and future model growth.

~$479 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run jointpreferences mistral 7b sft helpful?

Yes, Radeon PRO W7600 8GB can run jointpreferences mistral 7b sft helpful with a C grade (Tight fit). Expected decode speed: 39.8 tok/s.

How much VRAM does jointpreferences mistral 7b sft helpful need?

jointpreferences mistral 7b sft helpful (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for jointpreferences mistral 7b sft helpful?

The recommended quantization for jointpreferences mistral 7b sft helpful is Q4_K_M, which balances quality and memory efficiency.

What speed will jointpreferences mistral 7b sft helpful run at on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, jointpreferences mistral 7b sft helpful achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4865ms using Q4_K_M quantization.

Can Radeon PRO W7600 8GB run jointpreferences mistral 7b sft helpful for coding?

For coding workloads, jointpreferences mistral 7b sft helpful on Radeon PRO W7600 8GB receives a C grade with 39.8 tok/s and 40K context.

What context window can jointpreferences mistral 7b sft helpful use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, jointpreferences mistral 7b sft helpful can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7600 8GB See all hardware for jointpreferences mistral 7b sft helpful

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