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 4000 Ada brings 20 GB of ECC GDDR6 to the mid-range workstation segment — 4 GB more than any consumer Ada card at a comparable price tier. Built on Ada Lovelace with full professional driver support, it is well suited for sustained 13B inference and can handle many 30B models at Q4 quantization. The $1,250 price positions it as a practical workhorse for AI-enabled professional workstations that need certified reliability alongside genuine VRAM headroom.
Beyond LLMs
AI Capability Matrix
What AI tasks this GPU can handle — from text generation to image and video creation.
| Capability | Status | Representative Model | Detail |
|---|
| LLM Chat (7B) | Runs natively | Llama 3.1 8B Q4 | — |
| LLM Coding (30B) | Needs offload | Qwen 3 30B Q4 | — |
workstation-gradeecc-memoryprofessional-certifiedmid-workstation
Specifications
Compute
FP1627 TFLOPS
INT8432 TOPS
ArchitectureAda Lovelace
Memory
VRAM20 GB
Bandwidth360 GB/s
General
FamilyRTX Ada
SegmentWorkstation
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$1,250
Key Features
20 GB ECC GDDR6 VRAMAda Lovelace architecture with 4th-gen Tensor Cores and FP8 support27 TFLOPS FP16 computeISV-certified professional driversPCIe 4.0 x16 interface432 INT8 TOPS for quantized workloads
For AI Workloads
Strengths
- 20 GB ECC VRAM comfortably fits 13B models at FP16 and 30B models at Q4
- FP8 Tensor Core support enables efficient quantized inference not available on Ampere workstation cards
- Professional driver certification provides stability for production inference deployments
- More VRAM than any consumer RTX 4000-series card in the same price range
Considerations
- 27 TFLOPS FP16 is modest relative to the $1,250 price tag for pure AI throughput
- 360 GB/s bandwidth constrains decode throughput on larger models
- Consumer RTX 4070 Ti Super (16 GB, ~$800) offers competitive AI performance for less if ECC is not required
- 70B models remain out of reach even at aggressive quantization
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 35.5 tok/s · 56K ctx · llama.cppEST.
Qwen 3.5 9B 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, ollama, lm-studio.
Decode 42.9 tok/s · 71K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
397BTier 100Needs ~246.5 GB
123BTier 100Needs ~80.6 GB
1000BTier 100Needs ~616.6 GB
1000BTier 100Needs ~616.6 GB
1600BTier 100Needs ~865.8 GB
Image & Video Generation
Diffusion Model Compatibility
39 of 52 models can generate images or video on your RTX 4000 Ada 20GB
Upgrade paths
Upgrade from RTX 4000 Ada 20GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M1 Max 32GBNext step up
32 GB Unified (+12)400 GB/s (+40)
AUnlocks 17 additional models that do not fit on the current setup.Unlocks Qwen 3.5 35B A3B, Qwen 3 32B, EXAONE 4.0 32B+14 more
Unlocks 17 additional models that do not fit on the current setup.
~$2,499 MSRP
👁 IntelIntel Arc Pro B60 24GBBest value 24 GB VRAM (+4)456 GB/s (+96)
AUnlocks 22 additional models that do not fit on the current setup.Unlocks Qwen 3.6 35B A3B, Qwen 3.5 35B A3B, Qwen 3 32B+19 more
Unlocks 22 additional models that do not fit on the current setup.
~$599 MSRP
24 GB VRAM (+4)1008 GB/s (+648)
AUnlocks 22 additional models that do not fit on the current setup.Unlocks Qwen 3.6 35B A3B, Qwen 3.5 35B A3B, Qwen 3 32B+19 more · +43% faster avg
Unlocks 22 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 43%.
~$1,999 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+268)8000 GB/s (+7640)
BUnlocks 67 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+64 more · +250% faster avg
Unlocks 67 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 250%.
~$8,000 MSRP
Frequently Asked Questions
20
GB
RTX 4000 Ada 20GBCategory AvgMacBook Pro M1 Max 32GB
LLM Large (70B)
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~11.8s per image |
| Image Gen (Flux) | Very constrained | Flux.1 Dev FP16 | ~~53.3s per image |
| Image Gen (SD 3.5) | Tight fit | SD 3.5 Large FP16 | ~~1m 5s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~30.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~30.3s/frame |
Qwen 3.5 9B is a specialized fit for Agentic 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, ollama, lm-studio.
Decode 42.9 tok/s · 71K ctx · llama.cppEST.
Qwen 3 14B matches Reasoning 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 35.5 tok/s · 56K ctx · 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 48.3 tok/s · 69K ctx · llama.cppEST.
Image
| MAGI-1Video | 256×256 | ~27.8s/frame | F |
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.
Buying advice
Should you buy RTX 4000 Ada 20GB for local AI?
Good for local AI
Handles 21 of 50 top models. Smaller and mid-size models run comfortably.
What will limit you first
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best upgrade itinerary
Unlocks 17 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M1 Max 32GB (32.0 GB unified memory) is the next step up.
