NVIDIA
RTX PRO 5000 Blackwell 48GB
RTX PRO BlackwellWorkstationBlackwellPCIe 5CUDA
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 PRO 5000 Blackwell delivers 48 GB of ECC GDDR7 at 1,344 GB/s bandwidth with 96 TFLOPS FP16 and 2,500 INT8 TOPS — a major generational leap over the RTX 6000 Ada in both compute and memory bandwidth. Announced at GTC 2025 and shipping summer 2025, it comfortably handles 70B quantized inference on a single card and can support larger models with NVLink pairing. For professional AI workstations requiring maximum VRAM, ECC reliability, and certified driver support short of the flagship 96 GB tier, this is the sweet spot.
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) | Runs natively | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
workstation-gradeecc-memorylarge-vramprofessional-certifiedblackwellupcoming
Specifications
Compute
FP1696 TFLOPS
INT82500 TOPS
ArchitectureBlackwell
Memory
VRAM48 GB
Bandwidth1344 GB/s
General
FamilyRTX PRO Blackwell
SegmentWorkstation
InterconnectPCIe 5
Compute PlatformCUDA
MSRP$4,999
Key Features
48 GB ECC GDDR7 VRAMBlackwell 5th-gen Tensor Cores with FP4 and FP8 precision96 TFLOPS FP16 / 2,500 INT8 TOPS1,344 GB/s memory bandwidthPCIe 5.0 x16 interfaceNVLink support for multi-GPU configurations
For AI Workloads
Strengths
- 48 GB ECC VRAM runs 70B models at Q4 on a single GPU with good decode throughput thanks to 1,344 GB/s bandwidth
- 2,500 INT8 TOPS — roughly 70% more throughput than the RTX 6000 Ada — significantly improves quantized inference speed
- FP4 precision support enables the most aggressive quantization formats for maximum throughput
- NVLink allows two-card 96 GB pooled configuration for 70B FP16 or 100B+ inference
Considerations
- Shipping summer 2025 — not yet broadly available
- $4,999 price carries a workstation premium; consumer RTX 5090 (32 GB) offers high Blackwell performance at lower cost without ECC
- 70B FP16 still requires paired GPUs or the 96 GB flagship
- Premium only justified when ECC, ISV certification, or vGPU support are genuine requirements
Blackwell is NVIDIA's fifth-generation RTX architecture, built on TSMC's 4NP process. It introduces 5th-generation Tensor Cores with native FP4 precision support, enabling double the inference throughput per watt compared to Ada Lovelace's FP8 operations. Key innovations include the Neural Rendering Pipeline for AI-driven shading and the debut of GDDR7 memory in consumer GPUs.
AI Relevance
FP4 Tensor Cores deliver the highest tokens-per-watt efficiency in any consumer architecture. Native FP4 quantization means models can run at lower precision with minimal quality loss, effectively doubling the effective VRAM for model weights.
Process: TSMC 4NPPlatform: CUDATensor Cores: Gen 5Precisions: FP32, FP16, BF16, FP8, FP4, INT8, INT4
Recommendations by Workload
Qwen 3.6 35B A3B 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.
Decode 143.5 tok/s · 82K ctx · llama.cppEST.
Qwen 3.6 27B 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 36.0 tok/s · 262K 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 ~249.3 GB
123BTier 100Needs ~83.4 GB
1000BTier 100Needs ~619.4 GB
1000BTier 100Needs ~619.4 GB
1600BTier 100Needs ~868.6 GB
Image & Video Generation
Diffusion Model Compatibility
50 of 52 models can generate images or video on your RTX PRO 5000 Blackwell 48GB
Upgrade paths
Upgrade from RTX PRO 5000 Blackwell 48GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
AMD Instinct MI210 64GBNext step up
64 GB VRAM (+16)1638 GB/s (+294)
AUnlocks 5 additional models that do not fit on the current setup.Unlocks Llama 4 Scout 17B 16E, Command R+ 104B, Qwen3.5 122B A10B+2 more
Unlocks 5 additional models that do not fit on the current setup.
~$10,000 MSRP
80 GB VRAM (+32)2039 GB/s (+695)
AUnlocks 12 additional models that do not fit on the current setup.Unlocks Devstral 2 123B Instruct, Qwen 3.5 122B A10B, Mistral Small 4 119B+9 more · +16% faster avg
Unlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 16%.
~$15,000 MSRP
MacBook Pro M3 Max 128GBBest value
128 GB Unified (+80)
BUnlocks 13 additional models that do not fit on the current setup.Unlocks Devstral 2 123B Instruct, Qwen 3.5 122B A10B, Mistral Small 4 119B+10 more
Unlocks 13 additional models that do not fit on the current setup.
~$2,499 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+240)8000 GB/s (+6656)
BUnlocks 26 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+23 more · +86% faster avg
Unlocks 26 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 86%.
~$8,000 MSRP
Frequently Asked Questions
RTX PRO 5000 Blackwell 48GBCategory AvgAMD Instinct MI210 64GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~3.1s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~13.9s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~17s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~2.7s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~7.9s/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 fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, lm-studio.
Decode 36.0 tok/s · 262K ctx · 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 fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.
Decode 52.0 tok/s · 109K ctx · llama.cppEST.
Qwen 3.5 27B matches RAG 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 57.8 tok/s · 102K ctx · llama.cppEST.
96
35B28.5 GB156 tok/s131K ctx
Image
| MAGI-1Video | 256×256 | ~7.3s/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 PRO 5000 Blackwell 48GB for local AI?
Excellent choice for local AI
Runs 29 of 50 top models well — a strong all-rounder for local inference.
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 5 additional models that do not fit on the current setup.
Want more headroom? AMD Instinct MI210 64GB (64.0 GB VRAM) is the next step up.
