Will It Run AI

Can blossom v1 baichuan 7b i1 run on RX 590 8GB?

YES — Tight Fit

C49Usable
Estimated from fit model

blossom v1 baichuan 7b i1 needs ~6.8 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
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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, 25.8 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used

Fit status

Tight fit

Decode

25.8 tok/s

TTFT

7510 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 v1 baichuan 7b i1 on RX 590 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: 25.8 tok/s decode · 7.5s TTFT (warm) · 64 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well25.8 tok/s4096 ms40K
CodingCTight fit25.8 tok/s7510 ms40K
Agentic CodingCRuns with offload25.8 tok/s10923 ms40K
ReasoningCTight fit25.8 tok/s8875 ms40K
RAGCRuns with offload25.8 tok/s13654 ms40K

Quantization options

How blossom v1 baichuan 7b i1 (7B params) fits at each quantization level on RX 590 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 v1 baichuan 7b i1 on your machine.

Run

lms load hf-mradermacher--blossom-v1-baichuan-7b-i1-gguf && lms server start

Upgrade-Optionen

Hardware, die blossom v1 baichuan 7b i1 gut ausführt

RX 9060 XT 16GBGünstige Wahl
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 83%.47.2 tok/s Dekodierung

Raises estimated decode speed by about 83%.

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

ca. $349 MSRP

RX 7700 XT 12GBBestes Preis-Leistungs-Verhältnis
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 135%.60.7 tok/s Dekodierung

Raises estimated decode speed by about 135%.

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

ca. $449 MSRP

RX 6700 XT 12GBAMD-Upgrade
12 GB VRAM (+4)384 GB/s (+128)
C
Raises estimated decode speed by about 81%.46.8 tok/s Dekodierung

Raises estimated decode speed by about 81%.

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

ca. $479 MSRP

Frequently asked questions

Can RX 590 8GB run blossom v1 baichuan 7b i1?

Yes, RX 590 8GB can run blossom v1 baichuan 7b i1 with a C grade (Tight fit). Expected decode speed: 25.8 tok/s.

How much VRAM does blossom v1 baichuan 7b i1 need?

blossom v1 baichuan 7b i1 (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for blossom v1 baichuan 7b i1?

The recommended quantization for blossom v1 baichuan 7b i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will blossom v1 baichuan 7b i1 run at on RX 590 8GB?

On RX 590 8GB, blossom v1 baichuan 7b i1 achieves approximately 25.8 tokens per second decode speed with a time-to-first-token of 7510ms using Q4_K_M quantization.

Can RX 590 8GB run blossom v1 baichuan 7b i1 for coding?

For coding workloads, blossom v1 baichuan 7b i1 on RX 590 8GB receives a C grade with 25.8 tok/s and 40K context.

What context window can blossom v1 baichuan 7b i1 use on RX 590 8GB?

On RX 590 8GB, blossom v1 baichuan 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 RX 590 8GBSee all hardware for blossom v1 baichuan 7b i1
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