RTX AdaWorkstationAda 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.
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 β 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 β from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | β |
| LLM Coding (30B) | Runs natively | Qwen 3 30B Q4 | β |
| LLM Large (70B) |
workstation-gradeecc-memoryprofessional-certifiedlarge-vram
Specifications
Compute
FP1640 TFLOPS
INT8640 TOPS
ArchitectureAda Lovelace
Memory
VRAM24 GB
Bandwidth432 GB/s
General
FamilyRTX Ada
SegmentWorkstation
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$2,000
Key Features
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
For AI Workloads
Strengths
- 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
Considerations
- 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
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
Recommendations by Workload
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 33.7 tok/s Β· 60K ctx Β· llama.cppEST.
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 24.4 tok/s Β· 48K ctx Β· llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~246.9 GB
123BTier 100Needs ~81.0 GB
1000BTier 100Needs ~617.0 GB
1000BTier 100Needs ~617.0 GB
1600BTier 100Needs ~866.2 GB
Image & Video Generation
Diffusion Model Compatibility
41 of 52 models can generate images or video on your RTX 4500 Ada 24GB
Upgrade paths
Upgrade from RTX 4500 Ada 24GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M4 Max 36GBNext step up
36 GB Unified (+12)
AUnlocks 1 additional models that do not fit on the current setup.Unlocks Gemma 4 31B
Unlocks 1 additional models that do not fit on the current setup.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+144)
AUnlocks 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· +22% faster avg
Unlocks 6 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 22%.
~$4,000 MSRP
Mac mini M4 64GBBest value
64 GB Unified (+40)
BUnlocks 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 (+7568)
BUnlocks 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· +213% faster avg
Unlocks 45 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 213%.
~$8,000 MSRP
Frequently Asked Questions
24
GB
RTX 4500 Ada 24GBCategory AvgMacBook Pro M4 Max 36GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~7.9s per image |
| Image Gen (Flux) | Runs with offload | Flux.1 Dev FP16 | ~~35.5s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~43.4s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~6.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~20.2s/frame |
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 17.0 tok/s Β· 69K ctx Β· llama.cppEST.
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 25.1 tok/s Β· 40K ctx Β· llama.cppEST.
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 58.7 tok/s Β· 93K ctx Β· llama.cppEST.
21B18.6 GB66 tok/s52K ctx
Image
| MAGI-1Video | 256Γ256 | ~18.5s/frame | F |
Image models estimated at 1024Γ1024 (28 steps, FP16). Video models estimated at 768Γ512 (25 frames, 30 steps, FP16). Actual performance varies with runtime and system load.
Buying advice
Should you buy RTX 4500 Ada 24GB for local AI?
Excellent choice for local AI
Runs 26 of 50 top models well β a strong all-rounder for local inference.
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.