GPU memory size · 13 cards·327 featured models fit
Best LLMs for 24 GB VRAM
Runs 27–31B and 35B MoE models well; 70B doesn't fit in 24 GB.
| # | Model | Speed on RTX 4090 | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 32.1 t/s | 32.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 197 t/s | 197 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed on RTX 4090 | Coding Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 32.1 t/s | 32.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 151 t/s | 151 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 197 t/s | 197 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed on RTX 4090 | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 32.1 t/s | 32.1 tok/s | Q4_K_M · 21.7 GB | |
| 3 | Qwen3.5 27BAlibaba Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB | |
| 5 | Q4_K_M · 19.3 GB · 197 t/s | 197 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed on RTX 4090 | Math Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Qwen3.6 35B A3BAlibaba Q4_K_M · 22.0 GB · 151 t/s | 151 tok/s | Q4_K_M · 22.0 GB | |
| 4 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 32.1 t/s | 32.1 tok/s | overall | Q4_K_M · 21.7 GB |
| 5 | Q4_K_M · 19.3 GB · 197 t/s | 197 tok/s | Q4_K_M · 19.3 GB |
| # | Model | Speed on RTX 4090 | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB | |
| 4 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 190 t/s | 190 tok/s | Q4_K_M · 13.1 GB | |
| 5 | LFM2.5 24B A2BLiquid AI Q4_K_M · 15.0 GB · 221 t/s | 221 tok/s | Q4_K_M · 15.0 GB |
| # | Model | Speed on RTX 4090 ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 201 t/s | 201 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Q4_K_M · 19.3 GB · 197 t/s | 197 tok/s | Q4_K_M · 19.3 GB | |
| 3 | GPT OSS 20BOpenAI Q4_K_M · 13.1 GB · 190 t/s | 190 tok/s | Q4_K_M · 13.1 GB | |
| 4 | Qwen3 30B A3B Thinking 2507Alibaba Q4_K_M · 19.4 GB · 173 t/s | 173 tok/s | Q4_K_M · 19.4 GB | |
| 5 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB |
| # | Model | Speed on RTX 4090 | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.8 27BAlibabaHow to run → Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 2 | Qwen3.6 27BAlibaba Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB | |
| 3 | Gemma 4 31B ITGoogle Q4_K_M · 21.7 GB · 32.1 t/s | 32.1 tok/s | Q4_K_M · 21.7 GB | |
| 4 | Gemma 4 26B A4B ITGoogle Q4_K_M · 16.5 GB · 171 t/s | 171 tok/s | Q4_K_M · 16.5 GB | |
| 5 | JEV 27B VLautotrust Q4_K_M · 17.9 GB · 37.6 t/s | 37.6 tok/s | Q4_K_M · 17.9 GB |
GPUs with 24 GB VRAM
Compare all GPUs →Popular cards come first. Picks need a widely used card and a reasonable price.
Speed, price and value
Speed on Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used; a card without a price for the view is shown at its MSRP. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better.
| Card | GB/s | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|---|
| 1,008 | about $1,599 | 103 | – | 22.9 | |
| 936 | about $1,499 | 95.7 | – | 27.3 | |
| 960 | about $1,130 | 90.6 | 8.0 | 25.5 | |
| 1,008 | about $1,999 | 103 | – | 22.9 | |
9 more cards with 24 GB · less common or unpriced cards, not picked | |||||
| Card | GB/s | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|---|
| 1,008 | about $3,000 | 103 | 3.4 | 22.9 | |
| 936 | about $1,500 | 95.7 | 6.4 | 27.3 | |
| 960 | about $1,000 | 90.6 | 9.1 | 25.5 | |
| 1,008 | about $1,600 | 103 | 6.4 | 22.9 | |
9 more cards with 24 GB · less common or unpriced cards, not picked | |||||
Cost per token: 24 GB against 16 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, used price
- $0.70
- Power
- $0.16
Renting costs as much as buying after ≈8,800 hours (≈6.0 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. 24 GB cards without a price for the view are drawn hollow at their MSRP; other cards without one are left out. How we calculate cost per token
Next sizes down and up, plus why they're worth checking out. Speeds on Qwen3.5 9B.
Models that fit in 24 GB VRAM
Featured only327 featured models · 242 run great. Best quant that fits with a 16K context, ordered by llmrun Score (100 = average model). Fit grades how well it runs, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Idefics3 8B Llama3Vision | Q4_K_M | 7.5 GB | 107.7 t/s | 131K | S93, Runs great | |
Eurus 2 7B PRIMEChat | Q4_K_M | 5.1 GB | 121.9 t/s | 4K | S95, Runs great | |
| Q4_K_M | 6.8 GB | 123.7 t/s | 33K | S95, Runs great | ||
| Q4_K_M | 7.8 GB | 89.8 t/s | 8K | S89, Runs great | ||
GLM Z1 32B 0414ChatFunctions | Q4_K_M | 20.9 GB | 30.5 t/s | 33K | B62, Decent | |
| Q4_K_M | 17.8 GB | 41.0 t/s | 41K | A75, Runs well | ||
EXAONE 4.5 33BVisionFunctions | Q4_K_S | 23.9 GB | 30.2 t/s | 262K | C39, Usable | |
Qwen2.5 Omni 3BChat | Q4_K_M | 6.0 GB | 166.7 t/s | — | S98, Runs great | |
Qwen3 VL 2B InstructVisionFunctions | Q4_K_M | 3.5 GB | 336.2 t/s | 262K | S100, Runs great | |
| Q4_K_M | 1.0 GB | 602.5 t/s | 2K | S100, Runs great | ||
Qwen3 VL 32B InstructVisionFunctions | Q4_K_M | 23 GB | 29.5 t/s | 262K | B46, Decent | |
Qwen3 32BChatFunctions | Q4_K_M | 22.6 GB | 30.0 t/s | 41K | B50, Decent | |
Dots.ocrVision | Q4_K_M | 2.6 GB | 279.1 t/s | 131K | S100, Runs great | |
Moondream3 PreviewVision | BF16 | 23.1 GB | 29.8 t/s | — | B46, Decent | |
Translategemma 4B ITVision | Q4_K_M | 5 GB | 214.3 t/s | — | S100, Runs great |
Tokens per second by model size. Grey rows need more than 24 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB103tok/s
- 14BQwen3 14B · Q4_K_M · 11.9 GB69.0tok/s
- 27–31BQwen3.8 27B · Q4_K_M · 17.9 GB37.6tok/s
- 35B MoEQwen3.6 35B A3B · Q4_K_M · 22.0 GB151tok/s
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Speed estimates for the RTX 4090: Q4_K_M, 4K context, single user. How we estimate
Macs keep 3.5 GB for the system, so a Mac needs 32 GB to match a 24 GB card (28.5 GB usable). They run the same models slower because memory bandwidth is lower.
| Device | Usable | Bandwidth | Speed on Qwen3.8 27B |
|---|---|---|---|
Mac Studio M1 Max (32 GB)M1 Max · 32 GB memory | 28.5 GB | 400 GB/s | 16.1 tok/s |
Mac Studio M2 Max (32 GB)M2 Max · 32 GB memory | 28.5 GB | 400 GB/s | 16.1 tok/s |
Mac Mini M2 Pro (32 GB)M2 Pro · 32 GB memory | 28.5 GB | 200 GB/s | 8.0 tok/s |
MacBook Pro 14" M1 Pro (32 GB)M1 Pro · 32 GB memory | 28.5 GB | 200 GB/s | 8.0 tok/s |
24 GB VRAM FAQ
What models can I run with 24 GB of VRAM?
Models up to the 35B MoE class. 242 of 327 featured models run great. The top pick is Qwen3.8 27B at Q4_K_M: it needs 17.9 GB and runs at about 37.6 tok/s on the RTX 4090. Gemma 4 26B A4B IT and NVIDIA Nemotron 3.5 Lightning 30B A3B BF16 also run well.
Is 24 GB enough for 70B models?
No. Llama 3.3 70B Instruct needs 51.3 GB at Q4, more than 24 GB. Rent a bigger card by the hour, or look at 48 GB VRAM.
What is the best 24 GB GPU for local LLMs?
It depends on budget. Best value new and used: the RX 7900 XTX (90.6 tok/s); Fastest: the RTX 4090 (103 tok/s). Speeds are on Qwen3.5 9B.
How fast is a 24 GB card?
On the RTX 4090, Qwen3.5 9B runs at about 103 tok/s and Qwen3.8 27B at about 37.6 tok/s. Older and cheaper cards are slower: speed follows memory bandwidth, not memory size.
Is renting a 24 GB GPU cheaper than buying one?
An RTX 4090 rents from $0.34/hr. Renting costs as much as buying after ≈8,800 hours (≈6.0 years at 4 h a day).
Is a 24 GB Mac the same as a 24 GB GPU?
No. A Mac keeps about 3.5 GB for the system, so you need a 32 GB Mac to match. It is also slower: the Mac Studio M1 Max (32 GB) runs Qwen3.8 27B at about 16.1 tok/s.