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214 models fit / Q4_K_M / 8,192 tokens of context

Best local LLMs for 24 GB VRAM

Every catalogued model that fits entirely in 24 GB, with no layers moved to system RAM. Speeds are estimated on the NVIDIA GeForce RTX 4090, the most common 24 GB card in Steam's hardware survey.

Quick answer
214 catalogued models fit in 24 GB at Q4_K_M with 8,192 tokens of context. The largest widely used one is Qwen3.6-35B-A3B, needing 23.1 GB and running at ~370 tokens per second on the NVIDIA GeForce RTX 4090.

“Best” is not a quality ranking. The picks are the largest widely downloaded model that fits, the largest that still answers at 30 tokens a second or more, and the most downloaded; the list puts current, widely downloaded releases first. Speeds are estimates from memory bandwidth, not benchmarks.

Is 24 GB of VRAM enough for a local LLM?

For models under 36B parameters, yes: all 211 in the catalogue fit entirely at Q4_K_M, the format most people download. None of the 51 models in the 36B and larger band fits. All 214 that fit answer at 30 tokens a second or more on the NVIDIA GeForce RTX 4090, which is faster than most people read. A longer conversation needs more memory than the 8,192 tokens counted here, and a smaller format than Q4_K_M needs less; the calculator sizes either.

Model sizeFit in 24 GBFor example
Under 4B parameters66 of 66Qwen3.5-2B · needs 2.32 GB
4B to 9B parameters58 of 58Qwen3.5-4B · needs 3.98 GB
9B to 16B parameters21 of 21Qwen3.5-9B · needs 7.05 GB
16B to 36B parameters66 of 66Qwen3.8-27B · needs 18.6 GB
36B and larger parameters0 of 51none fits entirely

Models that fit in 24 GB

ModelParametersNeedsSpareDecodeAnswer
Qwen3.8-27B
Qwen · Q4_K_M
28B18.6 GB5.45 GB~46 tok/sDetails
gemma-4-26B-A4B-it
Google · Q4_K_M
26B16.8 GB7.21 GB~257 tok/sDetails
gemma-4-31B-it
Google · Q4_K_M
31B22.2 GB1.83 GB~33 tok/sDetails
Qwen3.5-9B
Qwen · Q4_K_M
9.7B7.05 GB17.0 GB~141 tok/sDetails
Qwen3.5-4B
Qwen · Q4_K_M
4.7B3.98 GB20.0 GB~254 tok/sDetails
Qwen3.6-35B-A3B
Qwen · Q4_K_M
36B23.1 GB0.90 GB~370 tok/sDetails
gemma-4-12B-it
Google · Q4_K_M
12B9.54 GB14.5 GB~84 tok/sDetails
Qwen3.6-27B
Qwen · Q4_K_M
28B18.6 GB5.45 GB~46 tok/sDetails
Qwen3.5-2B
Qwen · Q4_K_M
2.3B2.32 GB21.7 GB~556 tok/sDetails
gemma-4-E4B-it
Google · Q4_K_M
8.0B5.86 GB18.1 GB~227 tok/sDetails
NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
NVIDIA · Q4_K_M
32B20.3 GB3.71 GB~35 tok/sDetails
NVIDIA-Nemotron-3-Nano-4B-BF16
NVIDIA · Q4_K_M
4.0B3.46 GB20.5 GB~257 tok/sDetails
gemma-4-E2B-it
Google · Q4_K_M
5.1B4.02 GB20.0 GB~599 tok/sDetails
Qwen3.5-0.8B
Qwen · Q4_K_M
873M1.46 GB22.5 GB~1217 tok/sDetails
Qwen3-VL-8B-Instruct
Qwen · Q4_K_M
8.8B7.04 GB17.0 GB~116 tok/sDetails
Qwen3.5-27B
Qwen · Q4_K_M
28B18.6 GB5.45 GB~46 tok/sDetails
North-Micro-Vision-Instruct
CohereLabs · Q4_K_M
2.5B2.99 GB21.0 GB~365 tok/sDetails
Qwen3.5-35B-A3B
Qwen · Q4_K_M
36B23.1 GB0.90 GB~370 tok/sDetails
granite-4.2-8b
IBM · Q4_K_M
8.8B7.49 GB16.5 GB~106 tok/sDetails
GLM-4.7-Flash
zai-org · Q4_K_M
31B20.4 GB3.59 GB~35 tok/sDetails
granite-4.1-3b
IBM · Q4_K_M
3.4B3.72 GB20.3 GB~247 tok/sDetails
LFM2.5-2.6B
LiquidAI · Q4_K_M
2.7B2.61 GB21.4 GB~397 tok/sDetails
Qwen3-VL-4B-Instruct
Qwen · Q4_K_M
4.4B4.72 GB19.3 GB~185 tok/sDetails
granite-4.1-30b
IBM · Q4_K_M
29B20.4 GB3.56 GB~35 tok/sDetails
Qwen3-VL-2B-Instruct
Qwen · Q4_K_M
2.1B3.05 GB21.0 GB~335 tok/sDetails
granite-4.2-3b
IBM · Q4_K_M
3.7B3.72 GB20.3 GB~247 tok/sDetails
LFM2.5-230M
LiquidAI · Q4_K_M
230M1.05 GB22.9 GB~2766 tok/sDetails
Qwen3-0.6B
Qwen · Q4_K_M
752M2.22 GB21.8 GB~515 tok/sDetails
gpt-oss-20b
OpenAI · Q4_K_M
21B13.8 GB10.2 GB~274 tok/sDetails
granite-4.2-30b
IBM · Q4_K_M
29B20.7 GB3.33 GB~35 tok/sDetails
granite-4.1-8b
IBM · Q4_K_M
8.8B7.49 GB16.5 GB~106 tok/sDetails
NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
NVIDIA · Q4_K_M
32B20.3 GB3.71 GB~35 tok/sDetails
LFM2.5-VL-3B
LiquidAI · Q4_K_M
3.1B2.85 GB21.1 GB~397 tok/sDetails
MiMo-V2.6-Distill-Qwen-9B
XiaomiMiMo · Q4_K_M
9.4B6.90 GB17.1 GB~141 tok/sDetails
Qwen3-4B-Instruct-2507
Qwen · Q4_K_M
4.0B4.72 GB19.3 GB~185 tok/sDetails
Ling-3.0-tiny
inclusionAI · Q4_K_M
7.9B5.85 GB18.1 GB~799 tok/sDetails
Qwen3-8B
Qwen · Q4_K_M
8.2B7.04 GB17.0 GB~116 tok/sDetails
Olmo-3-7B-Instruct
allenai · Q4_K_M
7.3B7.96 GB16.0 GB~99 tok/sDetails
Qwen3-4B
Qwen · Q4_K_M
4.0B4.51 GB19.5 GB~185 tok/sDetails
LFM2.5-8B-A1B
LiquidAI · Q4_K_M
8.5B6.06 GB17.9 GB~618 tok/sDetails
174 more fit. The NVIDIA GeForce RTX 4090 page lists every one.All 214 models

9 devices with 24 GB

The same models fit on every one of them. What changes is speed, which follows memory bandwidth: a card with twice the bandwidth decodes roughly twice as fast.

DeviceBandwidthMemoryWhere to find one
NVIDIA GeForce RTX 4090
speeds on this page
1008 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
AMD Radeon™ RX 7900 XTX960 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
NVIDIA GeForce RTX 3090936 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
Intel Arc Pro B60 24GB456 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
NVIDIA GeForce RTX 3090 Ti1008 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
NVIDIA GeForce RTX 5090 Laptop GPU896 GB/s24 GB, dedicatedAmazon ↗ · eBay (new and used) ↗
NVIDIA L4300 GB/s24 GB, dedicated—
Apple M2100 GB/s24 GB, unifiedAmazon ↗ · eBay (new and used) ↗
Apple M3100 GB/s24 GB, unifiedAmazon ↗ · eBay (new and used) ↗

Which card to buy, at every memory size →

Store links may pay us a commission. They never decide which models are listed — the memory arithmetic does.

What does not fit in 24 GB

Widely downloaded models that need more than 24 GB at Q4_K_M, and the smallest memory size that holds each one entirely. Each link shows what 24 GB can still do with it: a smaller format, or part of the model in system RAM at a lower speed.

3 more models fit in 32 GB. Best local LLMs for 32 GB →

llmbottleneck
catalogue 2026-10-03models 327devices 135