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URL: https://willitrunai.com/gpus/l4-24gb

⇱ AI Models for NVIDIA L4 24GB β€” What Runs on 24GB VRAM


NVIDIA

NVIDIA L4 24GB

Ada DatacenterDatacenterAda LovelacePCIe 4CUDA

Operating mode

Choose the operating mode for this hardware

Use this to bias workload recommendations toward responsiveness, background autonomy, lighter serving, or multi-GPU scale-out.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

See Full AI Tier List for NVIDIA L4 24GB β†’

About this GPU for AI

The NVIDIA L4 is a compact, ultra-low-power Ada Lovelace datacenter GPU designed for power-constrained cloud inference. At just 72W TDP and a single-slot form factor, it is the most dense-deployable NVIDIA accelerator for inference at 24 GB. Its Ada Lovelace Tensor Cores include FP8 support, giving it superior INT8 throughput relative to older Ampere 24 GB cards despite similar compute TFLOPS. Cloud providers favor it for its rack density and per-GPU cost efficiency. It handles 7B models comfortably and 13B with Q4 quantization.

Beyond LLMs

AI Capability Matrix

What AI tasks this GPU can handle β€” from text generation to image and video creation.

CapabilityStatusRepresentative ModelDetail
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4β€”
LLM Coding (30B)Runs nativelyQwen 3 30B Q4β€”
LLM Large (70B)
low-tdpultra-denseinference-optimizedcloud-available

Specifications

Compute
FP1630 TFLOPS
INT8485 TOPS
ArchitectureAda Lovelace
Memory
VRAM24 GB
Bandwidth300 GB/s
General
FamilyAda Datacenter
SegmentDatacenter
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$2,500

Key Features

24 GB GDDR6 VRAM300 GB/s memory bandwidthAda Lovelace architecture with FP8 Tensor Core support485 INT8 TOPS β€” strong INT8 inference throughput72W TDP β€” single-slot, half-height compatiblePCIe 4.0 x16

For AI Workloads

Strengths
  • 72W TDP enables ultra-dense GPU configurations β€” more GPUs per server than any other NVIDIA datacenter option
  • FP8 support from Ada Tensor Cores boosts quantized inference throughput over older Ampere alternatives
  • Strong INT8 TOPS (485) for serving quantized 7B–13B models at scale
  • Very cost-effective on cloud for mid-scale inference deployments
Considerations
  • 300 GB/s bandwidth is the lowest in the Ada datacenter lineup β€” generation speed is limited for larger models
  • 24 GB VRAM cannot fit 30B+ models even with aggressive quantization
  • No NVLink β€” scaling across GPUs requires PCIe, limiting multi-GPU model serving
  • FP16 compute (30 TFLOPS) trails what you'd expect given INT8 strength

Architecture

Ada Lovelace

Ada Lovelace is NVIDIA's fourth-generation RTX architecture, manufactured on TSMC's custom 4N process. It introduces 4th-generation Tensor Cores with FP8 support, 3rd-generation ray tracing cores, and the Shader Execution Reordering (SER) engine for improved workload scheduling.

AI Relevance

FP8 Tensor Core operations provide a significant uplift for quantized LLM inference compared to Ampere's FP16-only Tensor Cores. DLSS 3 Frame Generation demonstrates the architecture's AI processing capabilities.

Process: TSMC 4NPlatform: CUDATensor Cores: Gen 4Precisions: FP32, FP16, BF16, FP8, INT8, INT4

Cost vs cloud API

On par with cloud API pricing β€” local wins on privacy + latency

Assumes 4 hours/day of active inference at 31 tok/s, NVIDIA L4 24GB amortized over 36 months, US residential electricity ($0.15/kWh), blended cloud pricing at $10 per 1M tokens (GPT-4o / Claude Sonnet tier).

13.2M

Tokens/month at this pace

$70.7

Monthly local cost

$132

Same tokens on cloud API

$5.37

Local $/1M tokens

Break-even: amortizes in 19.2 months vs cloud API. Price reference: $2.5k MSRP.

Recommendations by Workload

Chat

S

Qwen 3 14B

Qwen 3 14B matches Chat and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 19.3 tok/s Β· 60K ctx Β· llama.cppEST.
16.0 GB / 24.0 GB VRAM

Coding

S

Codestral 2 25.08

Codestral 2 25.08 is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.

Decode 13.9 tok/s Β· 48K ctx Β· llama.cppEST.
19.2 GB / 24.0 GB VRAM

Agentic Coding

S

Full Model Compatibility

πŸ‘ Alibaba
Qwen3-VL 30B A3B Instruct
S93
30B23.1 GB31 tok/s26K ctx
moe
πŸ‘ Alibaba
Qwen3-Coder 30B A3B Instruct
S92
30.5B23.4 GB21 tok/s23K ctx
moe
πŸ‘ OpenAI
GPT-OSS 20B
S92

Just out of reach

Models you could run with an upgrade

High-quality models that need a bit more memory

Image & Video Generation

Diffusion Model Compatibility

41 of 52 models can generate images or video on your NVIDIA L4 24GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512Γ—512~1.6sS
Stable Diffusion 1.5Image512Γ—768~3.2sS
Realistic Vision v5.1Image512Γ—768~3.2sS
DreamShaper 8Image512Γ—768~3.2sS
LCM DreamShaper v7

Upgrade paths

Upgrade from NVIDIA L4 24GB

See what you unlock with more powerful hardware

Upgrade options

Upgrade options

MacBook Pro M4 Max 36GBNext step up
36 GB Unified (+12)410 GB/s (+110)
A
Unlocks 1 additional models that do not fit on the current setup.Unlocks Gemma 4 31B+42% faster avg

Unlocks 1 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 42%.

~$2,499 MSRP

πŸ‘ NVIDIA
RTX 5000 Ada 32GBNVIDIA upgrade
32 GB VRAM (+8)576 GB/s (+276)
A
Unlocks 6 additional models that do not fit on the current setup.Unlocks Gemma 4 31B, Kimi Linear 48B A3B, Falcon 40B Instruct+3 more Β· +98% faster avg

Unlocks 6 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 98%.

~$4,000 MSRP

Mac mini M4 64GBBest value
64 GB Unified (+40)
B
Unlocks 17 additional models that do not fit on the current setup.Unlocks Qwen 2.5 VL 72B, Gemma 4 31B, Llama 3.3 70B+14 more

Unlocks 17 additional models that do not fit on the current setup.

~$1,099 MSRP

AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+264)8000 GB/s (+7700)
B
Unlocks 45 additional models that do not fit on the current setup.Unlocks Qwen 3.5 397B A17B, Devstral 2 123B Instruct, Qwen 3.5 122B A10B+42 more Β· +405% faster avg

Unlocks 45 additional models that do not fit on the current setup.

Lifts average decode speed across fitting models by about 405%.

~$8,000 MSRP

Frequently Asked Questions

Compare with similar

NVIDIA L4 24GB vs RTX 3090 24GBNVIDIA L4 24GB vs RTX 3090 Ti 24GBNVIDIA L4 24GB vs RTX 4090 24GB
Compare this GPUCompare with another GPU β†’
24
GB
VRAM
300GB/s
Bandwidth
30TFLOPS
FP16 Compute
485TOPS
INT8 Inference
$2,500 MSRP
NVIDIA L4 24GBCategory AvgMacBook Pro M4 Max 36GB
Won’t fit
Llama 3.1 70B Q4
β€”
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16~~12.8s per image
Image Gen (Flux)Runs with offloadFlux.1 Dev FP16~~57.5s per image
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16~~1m 10s per image
Video Short (25f)Runs nativelyLTX Video 2B~~11.1s/frame
Video Long (100f)Won't fitWan Video 14B~~32.7s/frame

Qwen 3.6 27B

Qwen 3.6 27B is a specialized fit for Agentic Coding. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.

Decode 9.7 tok/s Β· 69K ctx Β· llama.cppEST.
21.7 GB / 24.0 GB VRAM

Reasoning

S

Devstral Small 2 24B Instruct

Devstral Small 2 24B Instruct matches Reasoning and keeps a practical fit profile. It is a recent-generation family, which helps on current local SOTA workloads. It should run, but memory headroom will be limited. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Decode 14.3 tok/s Β· 40K ctx Β· llama.cppEST.
20.4 GB / 24.0 GB VRAM

RAG

A

Granite 4.1 8B

Granite 4.1 8B matches RAG and keeps a practical fit profile. It sits in the middle of the current generation mix. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama.

Decode 33.6 tok/s Β· 93K ctx Β· llama.cppEST.
14.7 GB / 24.0 GB VRAM
21B18.6 GB34 tok/s52K ctx
moe
πŸ‘ Alibaba
Qwen 3 14B
S92
14B14.3 GB28 tok/s80K ctx
dense
πŸ‘ Microsoft
Phi-4-reasoning-plus 14B
S91
14.7B15.3 GB24 tok/s33K ctx
dense
πŸ‘ Alibaba
Qwen 3.5 9B
S90
9B11.0 GB35 tok/s111K ctx
dense
πŸ‘ Alibaba
Qwen 3 30B A3B
S90
30.5B23.4 GB21 tok/s23K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 27B
S89
27B22.9 GB9 tok/s21K ctx
dense
πŸ‘ Alibaba
Qwen 3 8B
S88
8B10.4 GB40 tok/s115K ctx
dense
πŸ‘ Mistral
Magistral Small 2507
S88
24B20.4 GB10 tok/s40K ctx
dense
πŸ‘ Mistral
Devstral Small 2 24B Instruct
S88
24B20.4 GB10 tok/s40K ctx
dense
πŸ‘ Alibaba
Qwen 3.6 27B
S88
27B20.7 GB6 tok/s69K ctx
+1dense
πŸ‘ Alibaba
Qwen 3.5 4B
S88
4B7.9 GB64 tok/s131K ctx
dense
πŸ‘ NVIDIA
Nemotron Cascade 2 30B A3B
S87
30B24.5 GB22 tok/s13K ctx
moe
πŸ‘ Mistral
Devstral Small 1.1
S86
24B20.4 GB10 tok/s40K ctx
dense
πŸ‘ Mistral
Ministral 3 14B
S86
14B14.3 GB26 tok/s80K ctx
multimodal
πŸ‘ Google
Gemma 4 26B A4B
S85
25.2B22.3 GB29 tok/s23K ctx
moe
πŸ‘ NVIDIA
Nemotron 3 Nano 30B
A85
30B24.0 GB5 tok/s16K ctx
dense
πŸ‘ Microsoft
Phi-4 Mini Reasoning 4B
A84
3.8B7.1 GB61 tok/s131K ctx
dense
πŸ‘ NVIDIA
Nemotron Nano 8B
A83
8B10.1 GB40 tok/s130K ctx
dense
πŸ‘ Alibaba
Qwen 3.5 35B A3B
A80
35B26.1 GB18 tok/s4K ctx
moe
πŸ‘ Jina AI
Jina Embeddings v3
A77
0.57B6.4 GB9 tok/s8K ctx
dense
πŸ‘ BAAI
BGE M3
A75
0.57B5.6 GB9 tok/s8K ctx
dense
πŸ‘ Alibaba
Qwen 3 32B
A75
32B26.7 GB3 tok/s5K ctx
dense
πŸ‘ Alibaba
Qwen 3.6 35B A3B
A74
35B28.8 GB14 tok/s4K ctx
+1moe
πŸ‘ LG AI
EXAONE 4.0 32B
B69
32B26.7 GB4 tok/s5K ctx
dense
πŸ‘ Alibaba
Qwen 3.5 397B A17B
F0
397B248.3 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Devstral 2 123B Instruct
F0
123B83.7 GB2 tok/s4K ctx
dense
πŸ‘ Moonshot AI
Kimi K2.5
F0
1000B620.7 GB2 tok/s4K ctx
moe
πŸ‘ Moonshot AI
Kimi K2.6
F0
1000B620.7 GB2 tok/s4K ctx
+1moe
πŸ‘ DeepSeek
DeepSeek V4 Pro
F0
1600B867.2 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3.5 122B A10B
F0
122B80.2 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V4 Flash
F0
284B162.6 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Mistral Small 4 119B
F0
119B81.3 GB2 tok/s4K ctx
moe
πŸ‘ Cohere
Command A 111B
F0
111B74.9 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen 2.5 VL 72B
F0
72B52.1 GB2 tok/s4K ctx
dense
πŸ‘ OpenAI
GPT-OSS 120B
F0
117B79.6 GB2 tok/s4K ctx
dense
πŸ‘ Alibaba
Qwen3-Coder-Next
F0
80B53.6 GB2 tok/s4K ctx
moe
πŸ‘ Z.ai
GLM-5.1
F0
754B482.3 GB2 tok/s4K ctx
moe
πŸ‘ Mistral AI
Pixtral Large 124B
F0
124B84.3 GB2 tok/s4K ctx
dense
πŸ‘ Z.ai
GLM-5
F0
744B476.2 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.2
F0
671B413.1 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen 3 235B A22B
F0
235B149.5 GB2 tok/s4K ctx
moe
πŸ‘ Alibaba
Qwen3-Coder 480B A35B Instruct
F0
480B299.0 GB2 tok/s4K ctx
moe
πŸ‘ Google
Gemma 4 31B
F0
30.7B36.7 GB2 tok/s4K ctx
dense
MiniMax M2.7
F0
230B147.4 GB2 tok/s4K ctx
moe
πŸ‘ Mistral
Leanstral 119B A6B
F0
119B84.7 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek Coder V2 236B
F0
236B205.9 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek R1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe
πŸ‘ DeepSeek
DeepSeek V3.1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe
Image
512Γ—768
~1s
S
PixArt-SigmaImage1024Γ—1024~12.8sS
FramePack I2VVideo256Γ—256~23.5s/frameS
SDXL TurboImage512Γ—512~1.6sS
SDXL LightningImage1024Γ—1024~4.8sS
Stable Diffusion XL 1.0Image1024Γ—1024~12.8sS
Playground v2.5Image1024Γ—1024~19.2sS
RealVisXL v5.0Image1024Γ—1024~14.4sS
DreamShaper XLImage1024Γ—1024~14.4sS
Juggernaut XL v9Image1024Γ—1024~14.4sS
Animagine XL 3.1Image1024Γ—1024~14.4sS
Pony Diffusion V6 XLImage1024Γ—1024~14.4sS
Animagine XL 4.0Image1024Γ—1024~14.4sS
Illustrious XLImage1024Γ—1024~14.4sS
Wan Video 2.1 1.3BVideo256Γ—256~9.3s/frameS
Stable Diffusion 3.5 MediumImage1024Γ—1024~22.4sS
Flux.2 Klein 4BImage1024Γ—1024~3.8sS
LTX Video 2BVideo768Γ—512~11.1s/frameS
KolorsImage1024Γ—1024~25.6sS
Stable CascadeImage1024Γ—1024~32sS
AuraFlow v0.3Image1536Γ—1536~57.5sS
Stable Diffusion 3.5 LargeImage1024Γ—1024~1m 10sS
Stable Diffusion 3.5 Large TurboImage1024Γ—1024~12.8sS
CogVideoX 2BVideo720Γ—480~11.1s/frameA
HunyuanVideoVideo256Γ—256~23.5s/frameA
ChromaImage256Γ—256~23.5sA
Z-Image TurboImage1536Γ—1536~13.2sB
Flux.1 DevImage256Γ—256~57.5sB
Flux.1 SchnellImage256Γ—256~11.2sB
LTX Video 13BVideo256Γ—256~23.5s/frameB
Flux.1 Kontext DevImage256Γ—256~1m 4sB
AnimateDiff v1.5.3Video512Γ—768~5.8s/frameB
Cosmos Diffusion 7BVideo256Γ—256~35.3s/frameB
CogVideoX 5BVideo256Γ—256~33.6s/frameB
Wan2.2 TI2V 5BVideo256Γ—256~33.6s/frameB
Flux.2 Klein 9BImage256Γ—256~11.7sD
Flux.1 Fill DevImage256Γ—256~54.3sD
Mochi 1 PreviewVideo256Γ—256~21.1s/frameF
HunyuanVideo 1.5Video256Γ—256~19.6s/frameF
Helios 14BVideo256Γ—256~24.2s/frameF
SkyReels V2 14BVideo256Γ—256~24.2s/frameF
Wan Video 2.1 14BVideo256Γ—256~24.2s/frameF
Wan Video 2.2 14BVideo256Γ—256~24.2s/frameF
Qwen ImageImage256Γ—256~21.5sF
Qwen Image EditImage256Γ—256~21.5sF
Flux.2 DevImage256Γ—256~10m 5sF
MAGI-1Video256Γ—256~30s/frameF
HunyuanImage 3.0Image256Γ—256~37.9sF

Image models estimated at 1024Γ—1024 (28 steps, FP16). Video models estimated at 768Γ—512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.

Buying advice

Should you buy NVIDIA L4 24GB for local AI?

Excellent choice for local AI

Runs 26 of 50 top models well β€” a strong all-rounder for local inference.

24.0 GB

VRAM

$2,500

MSRP

$104/GB

Cost per GB VRAM

Best models for this GPU

What will limit you first

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 upgrade itinerary

Buy headroom, not only minimum fit

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

Unlocks 1 additional models that do not fit on the current setup.

Want more headroom? MacBook Pro M4 Max 36GB (36.0 GB unified memory) is the next step up.