NVIDIA

RTX 4500 Ada 24GB

RTX AdaWorkstationAda LovelacePCIe 4CUDA
24GB
VRAM
432GB/s
Bandwidth
40TFLOPS
FP16 Compute
640TOPS
INT8 Inference
$2,000 MSRP
VRAM24 GBBandwidth432 GB/sCompute40 TFInference640 TOPSValue2 TF/$k
RTX 4500 Ada 24GBCategory AvgMacBook Pro M4 Max 36GB

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.

About this GPU for AI

The RTX 4500 Ada delivers 24 GB of ECC GDDR6 with 40 TFLOPS FP16 compute in a professional workstation package. It matches the VRAM of the consumer RTX 3090 while adding ECC memory, ISV-certified drivers, and Ada's FP8 Tensor Core support — making it a compelling option for teams running 30B models or fine-tuning smaller models where data integrity matters. At $2,000 it costs roughly twice the consumer equivalent for the added professional guarantees.

Beyond LLMs

AI Capability Matrix

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

CapabilityStatusRepresentative Model
LLM Chat (7B)Runs nativelyLlama 3.1 8B Q4
LLM Coding (30B)Runs nativelyQwen 3 30B Q4
LLM Large (70B)Won’t fitLlama 3.1 70B Q4
Image Gen (SDXL)Runs nativelySDXL 1.0 FP16
Image Gen (Flux)Runs with offloadFlux.1 Dev FP16
Image Gen (SD 3.5)Runs nativelySD 3.5 Large FP16
Video Short (25f)Runs nativelyLTX Video 2B
Video Long (100f)Won't fitWan Video 14B
workstation-gradeecc-memoryprofessional-certifiedlarge-vram

Spezifikationen

Rechenleistung
FP1640 TFLOPS
INT8640 TOPS
ArchitekturAda Lovelace
Speicher
VRAM24 GB
Bandbreite432 GB/s
Allgemein
FamilieRTX Ada
SegmentWorkstation
InterconnectPCIe 4
Compute-PlattformCUDA
MSRP$2,000

Hauptmerkmale

24 GB ECC GDDR6 VRAMAda Lovelace 4th-gen Tensor Cores with FP8 support40 TFLOPS FP16 / 640 INT8 TOPS432 GB/s memory bandwidthISV-certified drivers with enterprise supportPCIe 4.0 x16, single 8-pin power

Für KI-Workloads

Stärken
  • 24 GB ECC VRAM fits 30B models at Q4 and 13B models at FP16 with room for context
  • ECC memory ensures data integrity for production inference where silent errors are unacceptable
  • FP8 support accelerates quantized inference beyond what Ampere-generation workstation cards offer
  • Professional driver stability suits long-running inference servers in workstation environments
Hinweise
  • Roughly twice the cost of consumer RTX 4090 (24 GB) for similar or lower raw AI throughput
  • 432 GB/s bandwidth is adequate but not exceptional for 30B decode speed
  • 70B models do not fit at practical quantization levels on a single card
  • Premium over consumer cards is hard to justify for purely AI workloads without ISV software requirements

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

Kaufberatung

Sollten Sie RTX 4500 Ada 24GB für lokale KI kaufen?

Ausgezeichnete Wahl für lokale KI

Führt 26 von 50 Top-Modellen gut aus — ein starker Allrounder für lokale Inferenz.

24.0 GB

VRAM

$2,000

UVP

$83/GB

Kosten pro GB VRAM

Beste Modelle für diese 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.

Mehr Spielraum gewünscht? MacBook Pro M4 Max 36GB (36.0 GB unified memory) ist die nächste Stufe.

Recommendations by Workload

Chat

S

Qwen 3 14B

This model is a direct match for chat. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 43.2 tok/s · 80K ctx · llama.cppEST.
13.1 GB / 24.0 GB VRAM

Coding

S

Devstral Small 2 24B Instruct

This model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.

Decode 25.1 tok/s · 40K ctx · llama.cppEST.
20.4 GB / 24.0 GB VRAM

Agentic Coding

S

Qwen 3.6 27B

This model is still usable for agentic-coding, but it is not the most specialized pick. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, lm-studio.

Decode 17.0 tok/s · 69K ctx · llama.cppEST.
21.7 GB / 24.0 GB VRAM

Reasoning

S

Qwen 3 14B

This model is a direct match for reasoning. It belongs to a current frontier family for local AI. It fits natively with comfortable headroom. Known channels: huggingface, ollama, lm-studio.

Decode 43.2 tok/s · 80K ctx · llama.cppEST.
14.3 GB / 24.0 GB VRAM

RAG

A

Granite 4.1 8B

This model is a direct match for rag. It sits in the middle of the current model mix. It fits natively with comfortable headroom. Known channels: huggingface, ollama.

Decode 75.2 tok/s · 104K ctx · llama.cppEST.
13.1 GB / 24.0 GB VRAM

Full Model Compatibility

AlibabaQwen3-Coder 30B A3B Instruct
S95
30.5B23.4 GB52 tok/s23K ctx
moe
AlibabaQwen3-VL 30B A3B Instruct
S94
30B23.1 GB53 tok/s26K ctx
moe
OpenAIGPT-OSS 20B
S94
21B18.6 GB66 tok/s52K ctx
moe
AlibabaQwen 3 14B
S93
14B14.3 GB43 tok/s80K ctx
dense
MicrosoftPhi-4-reasoning-plus 14B
S93
14.7B15.3 GB41 tok/s33K ctx
dense
AlibabaQwen 3 30B A3B
S93
30.5B23.4 GB52 tok/s23K ctx
moe
AlibabaQwen 3.5 9B
S92
9B11.0 GB67 tok/s111K ctx
dense
AlibabaQwen 3.5 27B
S92
27B22.9 GB22 tok/s21K ctx
dense
MistralMagistral Small 2507
S91
24B20.4 GB25 tok/s40K ctx
dense
MistralDevstral Small 2 24B Instruct
S91
24B20.4 GB25 tok/s40K ctx
dense
AlibabaQwen 3.6 27B
S91
27B20.7 GB17 tok/s69K ctx
+1dense
AlibabaQwen 3 8B
S90
8B10.4 GB75 tok/s115K ctx
dense
MistralDevstral Small 1.1
S89
24B20.4 GB25 tok/s40K ctx
dense
NVIDIANemotron Cascade 2 30B A3B
S89
30B24.5 GB38 tok/s13K ctx
moe
NVIDIANemotron 3 Nano 30B
S88
30B24.0 GB15 tok/s16K ctx
dense
AlibabaQwen 3.5 4B
S87
4B7.9 GB56 tok/s131K ctx
dense
GoogleGemma 4 26B A4B
S87
25.2B22.3 GB55 tok/s23K ctx
moe
MistralMinistral 3 14B
S87
14B14.3 GB43 tok/s80K ctx
multimodal
NVIDIANemotron Nano 8B
S85
8B10.1 GB75 tok/s130K ctx
dense
MicrosoftPhi-4 Mini Reasoning 4B
A84
3.8B7.1 GB53 tok/s131K ctx
dense
AlibabaQwen 3.5 35B A3B
A82
35B26.1 GB30 tok/s4K ctx
moe
AlibabaQwen 3 32B
A78
32B26.7 GB11 tok/s5K ctx
dense
Jina AIJina Embeddings v3
A77
0.57B6.4 GB8 tok/s8K ctx
dense
AlibabaQwen 3.6 35B A3B
A76
35B28.8 GB22 tok/s4K ctx
+1moe
BAAIBGE M3
A75
0.57B5.6 GB8 tok/s8K ctx
dense
LG AIEXAONE 4.0 32B
A72
32B26.7 GB11 tok/s5K ctx
dense
AlibabaQwen 3.5 397B A17B
F0
397B248.3 GB2 tok/s4K ctx
moe
MistralDevstral 2 123B Instruct
F0
123B83.7 GB2 tok/s4K ctx
dense
Moonshot AIKimi K2.5
F0
1000B620.7 GB2 tok/s4K ctx
moe
Moonshot AIKimi K2.6
F0
1000B620.7 GB2 tok/s4K ctx
+1moe
DeepSeekDeepSeek V4 Pro
F0
1600B867.2 GB2 tok/s4K ctx
moe
AlibabaQwen 3.5 122B A10B
F0
122B80.2 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V4 Flash
F0
284B162.6 GB2 tok/s4K ctx
moe
MistralMistral Small 4 119B
F0
119B81.3 GB2 tok/s4K ctx
moe
CohereCommand A 111B
F0
111B74.9 GB2 tok/s4K ctx
dense
AlibabaQwen 2.5 VL 72B
F0
72B52.1 GB2 tok/s4K ctx
dense
OpenAIGPT-OSS 120B
F0
117B79.6 GB2 tok/s4K ctx
dense
AlibabaQwen3-Coder-Next
F0
80B53.6 GB4 tok/s4K ctx
moe
Z.aiGLM-5.1
F0
754B482.3 GB2 tok/s4K ctx
moe
Mistral AIPixtral Large 124B
F0
124B84.3 GB2 tok/s4K ctx
dense
Z.aiGLM-5
F0
744B476.2 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.2
F0
671B413.1 GB2 tok/s4K ctx
moe
AlibabaQwen 3 235B A22B
F0
235B149.5 GB2 tok/s4K ctx
moe
AlibabaQwen3-Coder 480B A35B Instruct
F0
480B299.0 GB2 tok/s4K ctx
moe
GoogleGemma 4 31B
F0
30.7B36.7 GB5 tok/s4K ctx
dense
MiniMax M2.7
F0
230B147.4 GB2 tok/s4K ctx
moe
MistralLeanstral 119B A6B
F0
119B84.7 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek Coder V2 236B
F0
236B205.9 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek R1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe
DeepSeekDeepSeek V3.1 671B
F0
671B472.2 GB2 tok/s4K ctx
moe

Fast erreichbar

Modelle, die Sie mit einem Upgrade ausführen könnten

Hochwertige Modelle, die etwas mehr Speicher benötigen

1000BStufe 100Benötigt ca. 617.0 GB
1000BStufe 100Benötigt ca. 617.0 GB

Image & Video Generation

Diffusion Model Compatibility

41 of 52 models can generate images or video on your RTX 4500 Ada 24GB

ModelMax ResolutionGen TimeGrade
SD TurboImage512×512~1sS
Stable Diffusion 1.5Image512×768~2sS
Realistic Vision v5.1Image512×768~2sS
DreamShaper 8Image512×768~2sS
LCM DreamShaper v7Image512×768600msS
PixArt-SigmaImage1024×1024~7.9sS
FramePack I2VVideo256×256~14.5s/frameS
SDXL TurboImage512×512~1sS
SDXL LightningImage1024×1024~3sS
Stable Diffusion XL 1.0Image1024×1024~7.9sS
Playground v2.5Image1024×1024~11.8sS
RealVisXL v5.0Image1024×1024~8.9sS
DreamShaper XLImage1024×1024~8.9sS
Juggernaut XL v9Image1024×1024~8.9sS
Animagine XL 3.1Image1024×1024~8.9sS
Pony Diffusion V6 XLImage1024×1024~8.9sS
Animagine XL 4.0Image1024×1024~8.9sS
Illustrious XLImage1024×1024~8.9sS
Wan Video 2.1 1.3BVideo256×256~5.8s/frameS
Stable Diffusion 3.5 MediumImage1024×1024~13.8sS
Flux.2 Klein 4BImage1024×1024~2.4sS
LTX Video 2BVideo768×512~6.8s/frameS
KolorsImage1024×1024~15.8sS
Stable CascadeImage1024×1024~19.7sS
AuraFlow v0.3Image1536×1536~35.5sS
Stable Diffusion 3.5 LargeImage1024×1024~43.4sS
Stable Diffusion 3.5 Large TurboImage1024×1024~7.9sS
CogVideoX 2BVideo720×480~6.8s/frameA
HunyuanVideoVideo256×256~14.5s/frameA
ChromaImage256×256~14.5sA
Z-Image TurboImage1536×1536~8.1sB
Flux.1 DevImage256×256~35.5sB
Flux.1 SchnellImage256×256~6.9sB
LTX Video 13BVideo256×256~14.5s/frameB
Flux.1 Kontext DevImage256×256~39.4sB
AnimateDiff v1.5.3Video512×768~3.6s/frameB
Cosmos Diffusion 7BVideo256×256~21.8s/frameB
CogVideoX 5BVideo256×256~20.8s/frameB
Wan2.2 TI2V 5BVideo256×256~20.8s/frameB
Flux.2 Klein 9BImage256×256~7.2sD
Flux.1 Fill DevImage256×256~33.5sD
Mochi 1 PreviewVideo256×256~13s/frameF
HunyuanVideo 1.5Video256×256~12.1s/frameF
Helios 14BVideo256×256~14.9s/frameF
SkyReels V2 14BVideo256×256~14.9s/frameF
Wan Video 2.1 14BVideo256×256~14.9s/frameF
Wan Video 2.2 14BVideo256×256~14.9s/frameF
Qwen ImageImage256×256~13.3sF
Qwen Image EditImage256×256~13.3sF
Flux.2 DevImage256×256~6m 13sF
MAGI-1Video256×256~18.5s/frameF
HunyuanImage 3.0Image256×256~23.4sF

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

Upgrade paths

Upgrade from RTX 4500 Ada 24GB

See what you unlock with more powerful hardware

Upgrade-Optionen

Upgrade-Optionen

Frequently Asked Questions

What AI models can I run on RTX 4500 Ada 24GB?

RTX 4500 Ada 24GB (24 GB VRAM) can run these top models: Qwen3-Coder 30B A3B Instruct (score: 95/100), Qwen3-VL 30B A3B Instruct (score: 94/100), GPT-OSS 20B (score: 94/100). See the full compatibility list above.

How much VRAM does RTX 4500 Ada 24GB have for AI?

RTX 4500 Ada 24GB has 24 GB of VRAM available for AI model inference. This determines which models and quantization levels you can run locally.

Is RTX 4500 Ada 24GB good for running LLMs locally?

Yes, RTX 4500 Ada 24GB is excellent for running LLMs locally with top compatibility scores above 80/100.

What is the best model for RTX 4500 Ada 24GB for coding?

For coding on RTX 4500 Ada 24GB, we recommend Devstral Small 2 24B Instruct. It achieves 25.1 tokens per second with 40K context window. This model is a direct match for coding. It belongs to a current frontier family for local AI. It should run, but memory headroom will be limited. Known channels: huggingface, ollama, lm-studio.

Should I upgrade from RTX 4500 Ada 24GB?

There are 4 upgrade path(s) from RTX 4500 Ada 24GB: MacBook Pro M4 Max 36GB, RTX 5000 Ada 32GB. Upgrading would unlock larger models and faster inference speeds.

Can RTX 4500 Ada 24GB run Flux for image generation?

Yes, RTX 4500 Ada 24GB with 24 GB of usable memory can run Flux.1 Dev at FP16 natively. Flux is a 12B parameter diffusion transformer that produces high-quality images. You can also run the Schnell variant for faster generation.

What image and video AI models can I run on RTX 4500 Ada 24GB?

RTX 4500 Ada 24GB (24 GB VRAM) can handle various AI generation tasks beyond LLMs. For image generation, SDXL and Stable Diffusion 3.5 run well. Flux.1 Dev also runs natively for state-of-the-art image quality. For video, LTX Video 2.3 can generate short clips. Check the AI Capability Matrix above for detailed compatibility.

Is RTX 4500 Ada 24GB good for AI image generation?

RTX 4500 Ada 24GB is excellent for AI image generation. With 24 GB of usable memory, it runs all major diffusion models including Flux.1, SDXL, and Stable Diffusion 3.5 at full precision. You can generate high-resolution images quickly and even handle video generation models.

Can RTX 4500 Ada 24GB run Qwen 3.5 27B?

Yes, RTX 4500 Ada 24GB with 24 GB of usable memory can run Qwen 3.5 27B at Q4_K_M (~16.5 GB) with ~7 GB headroom for context and runtime. Quality at Q4 is very close to full precision for most tasks. Run it with: ollama run qwen3.5:27b

What is the best quantization for AI models on RTX 4500 Ada 24GB?

With 24 GB on RTX 4500 Ada 24GB, Q4_K_M is the sweet spot for 27B-35B models, Q6_K for 14B models, and Q8_0 for 8B-9B models. The general rule: use the highest quantization that fits with at least 2-3 GB headroom for KV cache and runtime.

For local LLMs on RTX 4500 Ada 24GB, does VRAM matter more than bandwidth?

RTX 4500 Ada 24GB has enough memory for many local LLMs, but bandwidth still matters a lot for real speed. Once a model fits, a faster-memory GPU can feel significantly better than a slower setup with similar capacity.

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