Tesla T4 16GB LLM Calculator — What Can It Run?
Check which models fit on a Tesla T4 16GB: max parameters per precision, cache headroom and estimated tokens/sec.
Tesla T4 16GB: 16 GB, 320 GB/s, ~65 TFLOPS FP16. The T4 is everywhere — free Colab, every cloud's cheapest GPU tier — with 16 GB and INT8 tensor cores. Its 70 W envelope made it the default inference card of the late-2010s; for LLMs it is now the 'will it run on free tier?' question.
Formula
About Tesla T4 16GB LLM Calculator — What Can It Run?
"Will it run?" is the first question of local AI, and for the Tesla T4 16GB this calculator answers it precisely: enter any model's parameter count and quantization and get the memory bill against this card's 16 GB, the largest model it can hold at that quant, and a bandwidth-derived decode-speed estimate (token generation streams the whole model per token, so 320 GB/s is the speed limit that matters). The T4 is everywhere — free Colab, every cloud's cheapest GPU tier — with 16 GB and INT8 tensor cores. Its 70 W envelope made it the default inference card of the late-2010s; for LLMs it is now the 'will it run on free tier?' question.
How to use Tesla T4 16GB LLM Calculator — What Can It Run?
- 1Enter your values into Tesla T4 16GB LLM Calculator — What Can It Run? — sensible, domain-typical defaults are pre-filled so you see a real result immediately.
- 2The result recomputes live using the formula shown on the page; there is no button to press.
- 3Adjust any input to compare scenarios, then read the worked example to see the substituted numbers.
Why use Tesla T4 16GB LLM Calculator — What Can It Run??
- ✓Computes Tesla T4 16GB LLM instantly in your browser — no sign-up, no upload, no server round-trip.
- ✓100% free and unlimited, with the exact formula shown: needed = params × bpw ÷ 8 + reserve.
- ✓Runs entirely client-side, so every value you enter stays private on your device.
- ✓Live recompute as you type, with a worked example and authoritative references for trust.
Frequently asked questions
What LLMs run on free Colab's T4?+
7–8B models at 4-bit (~4.5–5 GB) run comfortably with usable context; 13B Q4 (~7.5 GB) fits too. FP16 7B (~15 GB) technically loads but leaves almost no cache room. The T4 lacks BF16 — use FP16 or load_in_4bit, not bfloat16.
Why is the T4 slow for generation despite 65 TFLOPS?+
Its 65 TFLOPS are INT8/FP16 tensor-core peak for batched matmuls; single-stream decode is bound by the 320 GB/s memory bus. Expect 8–15 tokens/s on 7B Q4 — fine for experimentation, which is exactly what Colab's free tier is for.
How is the tokens/sec estimate for the Tesla T4 16GB derived?+
Decode is memory-bound: each token reads every weight once, so speed ≈ effective bandwidth ÷ model size. We assume ~60% of the 320 GB/s peak is achievable, matching llama.cpp benchmarks within ~20%. Prompt prefill is compute-bound and much faster per token.
Why reserve memory beyond the weights?+
The KV cache grows with context (use our per-model KV-cache calculators), CUDA/Metal runtimes take hundreds of MB, and allocator fragmentation wastes more. The default reserve suits 2–8K contexts; long-context work needs significantly more.
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