Data center GPU · NVIDIA
What AI models can an L4 run?
24 GB is the enthusiast standard, and it shows: a 32B model fits at a good quantization with a long context, and a 70B is reachable if you are willing to drop precision. At 300 GB/s there is enough bandwidth to keep generation responsive for anything that fits.
The short answer
Assuming an 8k context window and default settings, these are the models worth downloading first.
Best all-rounder
Gemma 4 26B A4B Instruct
Q6_K · 20.9 GB · about 34.8 tokens/s
Best for code
Qwen3 Coder 30B A3B Instruct
Q5_K_M · 21.11 GB · about 43.5 tokens/s
Largest that still runs well
Mixtral 8x7B Instruct V0.1
46.7B parameters · Q3_K_M · about 18.7 tokens/s
Fastest useful answer
LFM2.5 8B A1B
about 77.7 tokens/s · 8.81 GB
See how fast it feels
L4 running Gemma 4 26B A4B Instruct at Q6_K
YouWhy does my model use more memory when the conversation gets longer?
Reading your question
Simulated from our estimate at a 512-token question, not a recording. Assumes nothing else is competing for the GPU.
Where it sits
With 24 GB it belongs to the 24 GB tier. The 24 GB guide explains what that tier runs and where it runs out, and the bandwidth guide explains why 300 GB/s decides how fast it answers.
Our indicative price for this card is $2,200 new, reviewed 2026-09-01. That is 1.1 GB of memory and 14 GB/s of bandwidth for every $100. Prices move, so treat these as a place to start a search and not a quote.
Devices in a similar price band, fastest memory first:
| Device | Price | Memory | Bandwidth | Best all-round pick |
|---|---|---|---|---|
| GeForce RTX 5090 | $2,200 | 32 GB | 1792 GB/s | Gemma 4 31B Instruct, 48.5 t/s |
| GeForce RTX 4090 | $1,900 | 24 GB | 1008 GB/s | Qwen3.5 27B, 35.5 t/s |
Every model, scored on this device
Each row uses the highest-quality quantization that both fits and stays conversational. Speed is a single-stream estimate at 8k context.
| Model | Params | Verdict | Download | Memory used | Speed | Max context |
|---|---|---|---|---|---|---|
| Gemma 4 26B A4B Instruct Gemma | 25.81B (3.74B active) | Runs great | Q6_K | 20.9 GB | 34.8 t/s | 32k |
| GLM 4.7 Flash GLM | 31.22B (3.66B active) | Runs great | Q5_K_M | 21.22 GB | 45.5 t/s | 32k |
| GPT OSS 20B GPT-OSS | 20.91B (4.18B active) | Runs great | MXFP4 | 11.55 GB | 53.6 t/s | 128k |
| Qwen3.5 27B Qwen | 27.78B | Runs well | Q6_K | 21.33 GB | 10.6 t/s | 32k |
| Qwen3.5 35B A3B Qwen | 35.95B (2.9B active) | Runs great | Q4_K_M | 20.48 GB | 70.2 t/s | 128k |
| Qwen3 30B A3B Qwen | 30.53B (3.34B active) | Runs great | Q5_K_M | 21.11 GB | 43.5 t/s | 16k |
| Qwen3.5 9B Qwen | 9.65B | Runs well | Q8_0 | 9.25 GB | 25 t/s | 256k |
| Gemma 4 31B Instruct Gemma | 31.27B | Runs well | Q4_K_M | 21.31 GB | 10.6 t/s | 8k |
| Gemma 4 12B Instruct Gemma | 11.96B | Runs well | Q8_0 | 13.55 GB | 16.8 t/s | 64k |
| Qwen3 Coder 30B A3B Instruct Qwen | 30.53B (3.34B active) | Runs great | Q5_K_M | 21.11 GB | 43.5 t/s | 16k |
| Qwen3 32B Qwen | 32.76B | Runs well | Q4_K_M | 20.42 GB | 11 t/s | 16k |
| Granite 4.1 30B Granite | 28.87B | Runs well | Q5_K_M | 21.53 GB | 10.4 t/s | 8k |
| Qwen3 14B Qwen | 14.77B | Runs well | Q8_0 | 15.54 GB | 14.6 t/s | 32k |
| Gemma 4 E4B Instruct Gemma | 8B | Runs great | Q8_0 | 8.42 GB | 27.4 t/s | 128k |
| Granite 4.1 8B Granite | 8.79B | Runs well | Q8_0 | 10.35 GB | 22.2 t/s | 64k |
| Phi 3.5 MoE Instruct Phi | 41.87B (6.64B active) | Runs great | IQ4_XS | 22.06 GB | 29.9 t/s | 16k |
| Qwen3 8B Qwen | 8.19B | Runs great | Q8_0 | 9.02 GB | 25.6 t/s | 32k |
| Qwen2.5 14B Instruct Qwen | 14.77B | Runs well | Q8_0 | 15.79 GB | 14.4 t/s | 32k |
| Qwen2.5 32B Instruct Qwen | 32.76B | Runs well | Q4_K_M | 20.42 GB | 11 t/s | 16k |
| Gemma 3 12B Instruct Gemma | 12.19B | Runs well | Q8_0 | 13.27 GB | 17.1 t/s | 128k |
| Gemma 3 27B Instruct Gemma | 27.43B | Just fits | Q6_K | 22.48 GB | 10 t/s | 16k |
| Qwen2.5 Coder 14B Instruct Qwen | 14.77B | Runs well | Q8_0 | 15.79 GB | 14.4 t/s | 32k |
| Qwen2.5 Coder 32B Instruct Qwen | 32.76B | Runs well | Q4_K_M | 20.42 GB | 11 t/s | 16k |
| Qwen2.5 VL 7B Instruct Qwen | 8.29B | Runs great | Q8_0 | 8.49 GB | 27.2 t/s | 64k |
| Phi 4 Phi | 14.66B | Runs well | Q8_0 | 16 GB | 14.2 t/s | 16k |
| Mistral Small 24B Instruct 2501 Mistral | 23.57B | Runs well | Q6_K | 19.18 GB | 11.8 t/s | 32k |
| Mistral Nemo Instruct 2407 Mistral | 12.25B | Runs well | Q8_0 | 13.15 GB | 17.4 t/s | 64k |
| Mixtral 8x7B Instruct V0.1 Mistral | 46.7B (12.88B active) | Just fits | Q3_K_M | 22.6 GB | 18.7 t/s | 8k |
| Qwen2.5 7B Instruct Qwen | 7.62B | Runs great | Q8_0 | 7.83 GB | 29.7 t/s | 32k |
| Llama 3.1 8B Instruct Llama | 8.03B | Runs great | Q8_0 | 8.83 GB | 26.2 t/s | 64k |
Show the other 56 models
| DeepSeek Coder V2 Lite Instruct | 15.71B | Runs great | Q8_0 | 15.91 GB | 44 t/s | 128k |
| Gemma 2 9B Instruct | 9.24B | Runs well | Q8_0 | 11.5 GB | 19.9 t/s | 8k |
| Codestral 22B V0.1 | 22.25B | Runs well | Q6_K | 19.06 GB | 11.9 t/s | 16k |
| Qwen2.5 Coder 7B Instruct | 7.62B | Runs great | Q8_0 | 7.83 GB | 29.7 t/s | 32k |
| OLMo 3 7B Instruct | 7.3B | Runs well | Q8_0 | 9.72 GB | 23.7 t/s | 64k |
| Internlm3 8B Instruct | 8.8B | Runs great | Q8_0 | 8.97 GB | 25.8 t/s | 32k |
| Mistral 7B Instruct V0.3 | 7.25B | Runs great | Q8_0 | 8.45 GB | 27.4 t/s | 32k |
| Gemma 4 E2B Instruct | 5.12B | Runs great | Q8_0 | 5.44 GB | 43.1 t/s | 128k |
| Qwen3.5 4B | 4.66B | Runs great | Q8_0 | 5.27 GB | 45 t/s | 256k |
| Mistral 7B Instruct V0.2 | 7.24B | Runs great | Q8_0 | 8.44 GB | 27.5 t/s | 32k |
| DeepSeek Coder 7B Instruct V1.5 | 6.91B | Runs well | Q8_0 | 10.58 GB | 21.7 t/s | 4k |
| DeepSeek Coder 6.7B Instruct | 6.74B | Runs well | Q8_0 | 10.94 GB | 20.9 t/s | 16k |
| LFM2.5 8B A1B | 8.47B | Runs great | Q8_0 | 8.81 GB | 77.7 t/s | 64k |
| Qwen3 4B | 4.02B | Runs great | Q8_0 | 5.46 GB | 43.3 t/s | 32k |
| Gemma 3 4B Instruct | 4.3B | Runs great | Q8_0 | 4.91 GB | 48.5 t/s | 128k |
| Phi 4 Mini Instruct | 3.84B | Runs great | Q8_0 | 5.17 GB | 46 t/s | 128k |
| Granite 4.1 3B | 3.4B | Runs great | Q8_0 | 4.34 GB | 55.3 t/s | 128k |
| Qwen2.5 3B Instruct | 3.09B | Runs great | Q8_0 | 3.68 GB | 66.1 t/s | 32k |
| Phi 3 Mini 4k Instruct | 3.82B | Runs great | Q8_0 | 4.8 GB | 49.8 t/s | 4k |
| Llama 3.2 3B Instruct | 3.21B | Runs great | Q8_0 | 4.42 GB | 54.5 t/s | 128k |
| LFM2.5 2.6B | 2.7B | Runs great | Q8_0 | 3.14 GB | 78.9 t/s | 128k |
| Qwen3.5 2B | 2.27B | Runs great | Q8_0 | 2.7 GB | 93.6 t/s | 256k |
| Llama 3.3 70B Instruct | 70.55B | Fits only at 2 to 3 bits | IQ2_XXS | 19.71 GB | 11.5 t/s | 16k |
| Qwen2.5 72B Instruct | 72.71B | Fits only at 2 to 3 bits | IQ2_XXS | 20.18 GB | 11.2 t/s | 16k |
| Qwen2.5 VL 72B Instruct | 73.41B | Fits only at 2 to 3 bits | IQ2_XXS | 20.35 GB | 11.1 t/s | 16k |
| Gemma 2 2B Instruct | 2.61B | Runs great | Q8_0 | 3.54 GB | 69.1 t/s | 8k |
| Starcoder2 3B | 3.03B | Runs great | Q8_0 | 3.34 GB | 74.4 t/s | 16k |
| Qwen3 1.7B | 2.03B | Runs great | Q8_0 | 3.22 GB | 76.5 t/s | 32k |
| DeepSeek R1 Distill Qwen 1.5B | 1.78B | Runs great | Q8_0 | 2.07 GB | 126.2 t/s | 128k |
| Qwen2.5 1.5B Instruct | 1.54B | Runs great | Q8_0 | 2.06 GB | 126.7 t/s | 32k |
| SmolLM2 1.7B | 1.71B | Runs great | Q8_0 | 3.53 GB | 69.1 t/s | 8k |
| OLMo 2 0425 1B | 1.48B | Runs great | Q8_0 | 2.6 GB | 97.6 t/s | 4k |
| Llama 3.2 1B Instruct | 1.24B | Runs great | Q8_0 | 1.82 GB | 149.4 t/s | 128k |
| LFM2.5 1.2B Instruct | 1.17B | Runs great | Q8_0 | 1.46 GB | 197.3 t/s | 64k |
| Gemma 3 1B Instruct | 1B | Runs great | Q8_0 | 1.35 GB | 212.5 t/s | 32k |
| Qwen3.5 0.8B | 0.87B | Runs great | Q8_0 | 1.28 GB | 226.9 t/s | 256k |
| Qwen3 0.6B | 0.75B | Runs great | Q8_0 | 1.92 GB | 136.5 t/s | 32k |
| Qwen2.5 0.5B Instruct | 0.49B | Runs great | Q8_0 | 0.88 GB | 381.5 t/s | 32k |
| LFM2.5 350M | 0.35B | Runs great | Q8_0 | 0.68 GB | 590.7 t/s | 64k |
| Qwen3 Coder Next | 79.67B | Partial offload | n/a | 45.22 GB | 38.9 t/s | n/a |
| Qwen3 Next 80B A3B Instruct | 81.32B | Partial offload | n/a | 46.15 GB | 38.2 t/s | n/a |
| GPT OSS 120B | 116.83B | Partial offload | n/a | 59.4 GB | 19.4 t/s | n/a |
| Qwen3.5 122B A10B | 125.09B | Partial offload | n/a | 70.61 GB | 13.7 t/s | n/a |
| GLM 4.5 Air | 110.47B | Partial offload | n/a | 63.61 GB | 7.6 t/s | n/a |
| MiniMax M2.5 | 228.7B | Partial offload | n/a | 130.56 GB | 6.8 t/s | n/a |
| MiniMax M2.7 | 228.69B | Partial offload | n/a | 130.55 GB | 6.8 t/s | n/a |
| MiniMax M3 | 427.04B | Partial offload | n/a | 240.84 GB | 3.5 t/s | n/a |
| Qwen3 235B A22B | 235.09B | Partial offload | n/a | 133.71 GB | 3.5 t/s | n/a |
| GLM 4.5 | 358.34B | Partial offload | n/a | 204.37 GB | 2.5 t/s | n/a |
| DeepSeek V3.2 | 685.4B | Partial offload | n/a | 385.91 GB | 2.2 t/s | n/a |
| DeepSeek R1 | 684.53B | Partial offload | n/a | 385.42 GB | 2.2 t/s | n/a |
| Kimi K2.6 | 1026.88B | Partial offload | n/a | 577.78 GB | 2.1 t/s | n/a |
| Kimi K2 Instruct | 1026.41B | Partial offload | n/a | 577.52 GB | 2.1 t/s | n/a |
| GLM 5.1 | 753.86B | Partial offload | n/a | 424.51 GB | 2.1 t/s | n/a |
| GLM 5.2 | 753.33B | Partial offload | n/a | 424.21 GB | 1.8 t/s | n/a |
| Llama 3 3 Nemotron Super 49B V1 | 49.87B | Partial offload | n/a | 47.99 GB | 1.1 t/s | n/a |
What it will not run
0 of the 86 architectures we track are out of reach here, even at 2-bit precision. Another 17 run by splitting the model between the card and system RAM, which works but drops generation to single digits for most dense models.
If you want more headroom
The next steps up in memory, in order. More memory changes which models load at all; more bandwidth changes how fast they answer, so the two are worth weighing separately.
| Device | Memory | Bandwidth | Models that fit | |
|---|---|---|---|---|
| GeForce RTX 5090 | 32 GB | 1792 GB/s | 69 | Check price |
| Ryzen AI Max+ 395 32GB | 32 GB | 256 GB/s | 55 | Check price |
| Jetson AGX Orin 32GB | 32 GB | 204.8 GB/s | 56 | Check price |
| Intel Core Ultra 9 288V 32GB | 32 GB | 136 GB/s | 53 | Check price |
Price links go to an Amazon search for the model name and are affiliate links: if you buy through one, we earn a commission at no cost to you. We do not take payment for placement, and the ordering above is by memory capacity alone.
Other devices with 24 GB
Same capacity, different speed. Once a model fits, bandwidth is what separates these.
Questions people ask about an L4
How much VRAM does an L4 have?
The L4 has 24 GB of VRAM, of which roughly 23.2 GB is available to a model once the operating system takes its share.
Can an L4 run a 70B model?
Yes. At IQ2_XXS a 70B model fits in 19.71 GB and generates around 11.5 tokens per second.
What is the best local model for an L4?
Gemma 4 26B A4B Instruct at Q6_K is the strongest all-round fit: 20.9 GB used, around 34.8 tokens per second at 8k context.