Ada DatacenterDatacenterAda 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 NVIDIA L20 is a China-market Ada Lovelace inference GPU occupying the 48 GB VRAM tier with emphasis on INT8 throughput over raw FP16 compute. With 960 INT8 TOPS — higher than the L40S — the L20 was optimized for quantized inference pipelines. Its 864 GB/s bandwidth matches the L40S, and its Ada architecture brings FP8 support. The L20 targets cloud providers in China and APAC as a cost-efficient alternative to L40S for serving quantized 30B and smaller models at scale.
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) |
large-vraminference-optimizedpcie-form-factorenterprise-grade
Specifications
Compute
FP1660 TFLOPS
INT8960 TOPS
ArchitectureAda Lovelace
Memory
VRAM48 GB
Bandwidth864 GB/s
General
FamilyAda Datacenter
SegmentDatacenter
InterconnectPCIe 4
Compute PlatformCUDA
MSRP$5,500
Key Features
48 GB GDDR6 VRAM864 GB/s memory bandwidth60 TFLOPS FP16 / 960 INT8 TOPSAda Lovelace architecture with FP8 Tensor CoresPCIe 4.0 x16~320W TDP
For AI Workloads
Strengths
- 960 INT8 TOPS exceeds L40S — optimized for serving quantized models at high throughput
- 48 GB VRAM fits 30B models at Q4 and 13B models at FP16
- FP8 support from Ada Tensor Cores enables modern quantized inference frameworks
- Competitive pricing for APAC and China cloud deployments
Considerations
- Lower FP16 compute (60 TFLOPS) than L40S (91 TFLOPS) for FP16 workloads
- Primarily targeted at China/APAC market — limited availability elsewhere
- No NVLink support — multi-GPU scaling constrained to PCIe bandwidth
- GDDR6 bandwidth is a bottleneck compared to HBM alternatives at higher VRAM capacities
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 3.5 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 32.3 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 23.1 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 NVIDIA L20 48GB
Upgrade paths
Upgrade from NVIDIA L20 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 (+774)
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 · +18% faster avg
Unlocks 5 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 18%.
~$10,000 MSRP
80 GB VRAM (+32)2039 GB/s (+1175)
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 · +38% faster avg
Unlocks 12 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 38%.
~$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 (+7136)
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 · +121% faster avg
Unlocks 26 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 121%.
~$8,000 MSRP
Frequently Asked Questions
48
GB
NVIDIA L20 48GBCategory AvgAMD Instinct MI210 64GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~5.7s per image |
| Image Gen (Flux) | Runs natively | Flux.1 Dev FP16 | ~~25.6s per image |
| Image Gen (SD 3.5) | Runs natively | SD 3.5 Large FP16 | ~~31.3s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~4.9s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~14.6s/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 23.1 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 29.0 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 32.3 tok/s · 102K ctx · llama.cppEST.
30.5B25.8 GB69 tok/s256K ctx
Image
| MAGI-1Video | 256×256 | ~13.4s/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 NVIDIA L20 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.
