NVIDIA
Quadro RTX 8000 48GB
Quadro RTXWorkstationTuringPCIe 3CUDA
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 Quadro RTX 8000 was NVIDIA's most powerful Turing workstation GPU at launch, distinguished by its 48 GB of ECC GDDR6 — double the flagship consumer Turing card. Though based on the same Turing TU102 die as the RTX 6000, it doubles the VRAM to enable larger batch sizes and 70B quantized model inference on a single card, and with NVLink can scale to 96 GB. For teams still running Turing-era infrastructure, it remains a capable 70B inference platform, though modern Ada workstation cards now offer significantly better compute efficiency.
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-certifiednvlink-capablelegacy
Specifications
Compute
FP1632 TFLOPS
INT8256 TOPS
ArchitectureTuring
Memory
VRAM48 GB
Bandwidth672 GB/s
General
FamilyQuadro RTX
SegmentWorkstation
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$5,800
Key Features
48 GB ECC GDDR6 VRAMTuring TU102 die with 2nd-gen Tensor Cores (INT4, INT8, FP16)672 GB/s memory bandwidthNVLink support for 96 GB pooled VRAMISV-certified Quadro driversPCIe 3.0 x16 interface
For AI Workloads
Strengths
- 48 GB ECC VRAM fits 70B models at Q4 on a single card — a key differentiator among Turing-era hardware
- NVLink pairing enables 96 GB pooled VRAM for 70B FP16 inference
- Enterprise-certified drivers and ECC memory suit regulated production deployments
- Available used at dramatically reduced prices from original $5,800 MSRP
Considerations
- Turing Tensor Cores lack FP8 — inference efficiency lags well behind Ada and Blackwell alternatives
- 672 GB/s bandwidth limits 70B decode throughput despite the large VRAM
- PCIe 3.0 is a bottleneck for high-throughput multi-card or streaming inference configurations
- Aging platform with enterprise driver support potentially approaching end of life
Turing is NVIDIA's first-generation RTX architecture, introducing dedicated RT and Tensor Cores to consumer GPUs for the first time. Built on TSMC's 12nm FinFET process.
AI Relevance
The first consumer architecture with Tensor Cores, enabling meaningful acceleration for INT8 and FP16 inference. However, limited VRAM (typically 6-11 GB) restricts modern LLM model sizes.
Process: TSMC 12nmPlatform: CUDATensor Cores: Gen 2Precisions: FP32, FP16, INT8, INT4
Recommendations by Workload
Qwen 3.5 27B 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 23.7 tok/s · 102K 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 18.1 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 Quadro RTX 8000 48GB
Upgrade paths
Upgrade from Quadro RTX 8000 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 (+966)
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 · +40% faster avg
Unlocks 5 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 40%.
~$10,000 MSRP
80 GB VRAM (+32)2039 GB/s (+1367)
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 · +64% faster avg
Unlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 64%.
~$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 (+7328)
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 · +163% faster avg
Unlocks 26 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 163%.
~$8,000 MSRP
Frequently Asked Questions
48
GB
Quadro RTX 8000 48GBCategory AvgAMD Instinct MI210 64GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~11.3s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~50.8s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~1m 2s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~9.8s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~28.9s/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 18.1 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 21.4 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 23.7 tok/s · 102K ctx · llama.cppEST.
35B28.5 GB64 tok/s131K ctx
Image
| MAGI-1Video | 256×256 | ~26.5s/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 Quadro RTX 8000 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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.
