Turing DatacenterDatacenterTuringPCIe 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 NVIDIA T4 is a compact Turing-generation inference GPU built for data centers and cloud providers, featuring 16 GB of GDDR6 in a 70W single-slot passive form factor. It was the first NVIDIA accelerator to make INT8 inference a first-class use case, and it became the most widely deployed GPU for inference workloads in hyperscale cloud environments. While modest by current standards, it remains available on AWS (G4dn), GCP, and Azure at low cost per hour. It can handle 7B models with Q4 quantization but will struggle with anything larger.
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) | Won’t fit | Qwen 3 30B Q4 | — |
| LLM Large (70B) |
legacy-datacenterlow-tdpcloud-availableultra-dense
Specifications
Compute
FP1665 TFLOPS
INT8130 TOPS
ArchitectureTuring
Memory
VRAM16 GB
Bandwidth320 GB/s
General
FamilyTuring Datacenter
SegmentDatacenter
InterconnectPCIe 3
Compute PlatformCUDA
MSRP$2,200
Key Features
16 GB GDDR6 VRAM320 GB/s memory bandwidth65 TFLOPS FP16 (with sparsity) / 130 INT8 TOPSTuring architecture with 2nd-gen Tensor Cores70W TDP — single-slot passive coolingPCIe 3.0 x16
For AI Workloads
Strengths
- Extremely low 70W TDP enables the densest possible GPU configurations in standard servers
- Widely available on major cloud providers at the lowest per-hour GPU rates
- 16 GB VRAM is sufficient for 7B models at Q4 and smaller specialized models
- Proven in production inference at massive scale across AWS, GCP, and Azure
Considerations
- 16 GB VRAM cannot fit 13B models at any common quantization level
- Turing architecture lacks FP8 and modern sparsity optimizations
- 320 GB/s bandwidth results in slow generation speeds even for 7B models
- Obsolete compared to L4 (Ada) which offers better throughput at the same TDP and VRAM tier
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 9B 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 31.8 tok/s · 45K 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 31.8 tok/s · 45K ctx · llama.cppEST.
Just out of reach
Models you could run with an upgrade
High-quality models that need a bit more memory
30.5BTier 100Needs ~21.8 GB
397BTier 100Needs ~246.1 GB
123BTier 100Needs ~80.2 GB
1000BTier 100Needs ~616.2 GB
1000BTier 100Needs ~616.2 GB
Image & Video Generation
Diffusion Model Compatibility
31 of 52 models can generate images or video on your NVIDIA T4 16GB
Upgrade paths
Upgrade from NVIDIA T4 16GB
See what you unlock with more powerful hardware
Upgrade options
Upgrade options
MacBook Pro M3 24GBNext step up
24 GB Unified (+8)
CUnlocks 2 additional models that do not fit on the current setup.Unlocks Qwen 3.6 27B, Gemma 4 26B A4B
Unlocks 2 additional models that do not fit on the current setup.
~$1,099 MSRP
20 GB VRAM (+4)640 GB/s (+320)
BUnlocks 14 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+11 more · +88% faster avg
Unlocks 14 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 88%.
~$2,000 MSRP
👁 IntelIntel Arc Pro B60 24GBBest value 24 GB VRAM (+8)456 GB/s (+136)
AUnlocks 36 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 27B, Qwen 3.6 27B+33 more · +8% faster avg
Unlocks 36 additional models that do not fit on the current setup.
~$599 MSRP
AMD Instinct MI350X 288GBBiggest leap
288 GB VRAM (+272)8000 GB/s (+7680)
BUnlocks 81 additional models that do not fit on the current setup.Unlocks Qwen3-Coder 30B A3B Instruct, Qwen 3.5 397B A17B, Devstral 2 123B Instruct+78 more · +341% faster avg
Unlocks 81 additional models that do not fit on the current setup.
Lifts average decode speed across fitting models by about 341%.
~$8,000 MSRP
Frequently Asked Questions
16
GB
NVIDIA T4 16GBCategory AvgMacBook Pro M3 24GB
| Image Gen (SDXL) | Runs natively | SDXL 1.0 FP16 | ~~5.9s per image |
| Image Gen (Flux) | Won't fit | Flux.1 Dev FP16 | ~~26.6s per image |
| Image Gen (SD 3.5) | Runs with sequential offload | SD 3.5 Large FP16 | ~~1m 28s per image |
| Video Short (25f) | Runs natively | LTX Video 2B | ~~5.1s/frame |
| Video Long (100f) | Won't fit | Wan Video 14B | ~~15.1s/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 40.7 tok/s · 58K ctx · llama.cppEST.
Qwen 3.5 9B 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 31.8 tok/s · 45K 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 45.8 tok/s · 56K ctx · llama.cppEST.
14B
13.5 GB
26 tok/s
33K ctx
Image
| MAGI-1Video | 256×256 | ~13.8s/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.
There are 4 upgrade path(s) from NVIDIA T4 16GB: MacBook Pro M3 24GB, RTX A4500 20GB. Upgrading would unlock larger models and faster inference speeds.
Buying advice
Should you buy NVIDIA T4 16GB for local AI?
Usable for local AI with limits
Can run 11 of 50 top models, mostly smaller ones. Larger models need heavy quantization or won't fit.
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 2 additional models that do not fit on the current setup.
Want more headroom? MacBook Pro M3 24GB (24.0 GB unified memory) is the next step up.
