Will It Run AI

Can blossom v3 baichuan2 7b i1 run on Radeon Pro W7500 8GB?

YES — Tight Fit

C50Usable
Estimated from fit model

blossom v3 baichuan2 7b i1 needs ~6.8 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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, 31.0 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

31.0 tok/s

TTFT

6255 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 feelsblossom v3 baichuan2 7b i1 on Radeon Pro W7500 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: 31.0 tok/s decode · 6.3s TTFT (warm) · 77 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well31.0 tok/s3412 ms40K
CodingCTight fit31.0 tok/s6255 ms40K
Agentic CodingCRuns with offload31.0 tok/s9098 ms40K
ReasoningCTight fit31.0 tok/s7392 ms40K
RAGCRuns with offload31.0 tok/s11373 ms40K

Quantization options

How blossom v3 baichuan2 7b i1 (7B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC53
Q3_K_S
3
3.4 GB
LowC53
NVFP4
4
3.9 GB
MediumC53
Q4_K_M
4
4.3 GB
MediumC53
Q5_K_MBest for your GPU
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run blossom v3 baichuan2 7b i1 on your machine.

Run

lms load hf-mradermacher--blossom-v3-baichuan2-7b-i1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien blossom v3 baichuan2 7b i1

RX 7700 XT 12GBOpción económica
12 GB VRAM (+4)432 GB/s (+208)
C
Sube la velocidad estimada de decodificación alrededor de un 96%.60.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 96%.

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

~$449 MSRP

RX 6700 XT 12GBMejor relación calidad-precio
12 GB VRAM (+4)384 GB/s (+160)
C
Sube la velocidad estimada de decodificación alrededor de un 51%.46.8 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 51%.

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

~$479 MSRP

RX 9070 16GBMejora AMD
16 GB VRAM (+8)640 GB/s (+416)
C
Sube la velocidad estimada de decodificación alrededor de un 200%.92.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 200%.

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

~$479 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run blossom v3 baichuan2 7b i1?

Yes, Radeon Pro W7500 8GB can run blossom v3 baichuan2 7b i1 with a C grade (Tight fit). Expected decode speed: 31.0 tok/s.

How much VRAM does blossom v3 baichuan2 7b i1 need?

blossom v3 baichuan2 7b i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for blossom v3 baichuan2 7b i1?

The recommended quantization for blossom v3 baichuan2 7b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will blossom v3 baichuan2 7b i1 run at on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, blossom v3 baichuan2 7b i1 achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run blossom v3 baichuan2 7b i1 for coding?

For coding workloads, blossom v3 baichuan2 7b i1 on Radeon Pro W7500 8GB receives a C grade with 31.0 tok/s and 40K context.

What context window can blossom v3 baichuan2 7b i1 use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, blossom v3 baichuan2 7b i1 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 W7500 8GBSee all hardware for blossom v3 baichuan2 7b i1
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