Can Baichuan 13B run on RTX 3090 24GB?

YES — With Offload

B68Good

Estimated from fit model

Baichuan 13B needs ~24.9 GB VRAM. RTX 3090 24GB has 24.0 GB. With Q5_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: 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

Q5_K_M (High quality) — 24.9 GB, 48.7 tok/s, Runs with offload (needs ~0.3 GB host RAM)

24.9 GB required24.0 GB available

104% VRAM needed

0.9 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

48.7 tok/s

TTFT

3975 ms

Safe context

Memory

24.9 GB / 24.0 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsBaichuan 13B on RTX 3090 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	70.0 tok/s	1509 ms	8K
Coding	B	Runs with offload (needs ~0.3 GB host RAM)	48.7 tok/s	3975 ms	8K
Agentic Coding	F	Too heavy	21.0 tok/s	13403 ms	8K
Reasoning	B	Runs with offload (needs ~0.3 GB host RAM)	48.7 tok/s	4698 ms	8K
RAG	F	Too heavy	21.0 tok/s	16753 ms	8K

Inference speed

Baichuan 13B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Baichuan 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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?
RTX 5090 32GB	32 GB	Q5_K_M	130.8	Fits
RTX 4090 24GB	24 GB	Q5_K_M	61.0	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.7	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	52.4	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.6	Fits
RTX 3090 24GB	24 GB	Q5_K_M	48.7	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	33.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	33.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	24.0	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	20.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.2	Fits
RTX 4070 12GB	12 GB	Q5_K_M	7.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	4.5	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.2	Too big

Estimates for single-stream decoding at Q5_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 Baichuan 13B (13B params) fits at each quantization level on RTX 3090 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B62
Q3_K_S	3	6.4 GB	Low	B62
NVFP4	4	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B65
Q8_0Best for your GPU	8	13.9 GB	Very High	B66
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run Baichuan 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "baichuan-inc/Baichuan-13B-Chat" \
  --hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade-Optionen

Hardware, die Baichuan 13B gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+856)

Raises estimated decode speed by about 161%.126.9 tok/s Dekodierung

Raises estimated decode speed by about 161%.

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

ca. $1,999 MSRP

RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-Verhältnis

32 GB VRAM (+8)

Raises estimated decode speed by about 68%.82 tok/s Dekodierung

Raises estimated decode speed by about 68%.

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

ca. $2,499 MSRP

RTX 5000 Ada 32GBNVIDIA-Upgrade

32 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.50.2 tok/s Dekodierung

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

ca. $4,000 MSRP

Frequently asked questions

Can RTX 3090 24GB run Baichuan 13B?

Yes, RTX 3090 24GB can run Baichuan 13B with a B grade (Runs with offload (needs ~0.3 GB host RAM)). Expected decode speed: 48.7 tok/s.

How much VRAM does Baichuan 13B need?

Baichuan 13B (13B parameters) requires approximately 24.9 GB of memory with Q5_K_M quantization.

What is the best quantization for Baichuan 13B?

The recommended quantization for Baichuan 13B is Q5_K_M, which balances quality and memory efficiency.

What speed will Baichuan 13B run at on RTX 3090 24GB?

On RTX 3090 24GB, Baichuan 13B achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3975ms using Q5_K_M quantization.

Can RTX 3090 24GB run Baichuan 13B for coding?

For coding workloads, Baichuan 13B on RTX 3090 24GB receives a B grade with 48.7 tok/s and 8K context.

What context window can Baichuan 13B use on RTX 3090 24GB?

On RTX 3090 24GB, Baichuan 13B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan 13B feels slow on RTX 3090 24GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3090 24GB See all hardware for Baichuan 13B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/baichuan-13b-on-rtx-3090-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: