NVIDIA · Pascal · 8 GB VRAM tier
Best AI Models for NVIDIA GeForce GTX 1070 (8 GB)
Runs 7–9B models well; 14B doesn't fit in 8 GB.
8 GB at 256 GB/s: fits ~7-8B at Q4. Pascal: no Tensor Cores and very slow FP16, so it runs GGUF (llama.cpp, Ollama) fine but not FP16 runtimes like vLLM; current CUDA 13 builds dropped Pascal — use CUDA 12 or Vulkan builds.
| # | Model | Speed | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 26.2 t/s | 26.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 51.0 t/s | 51.0 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 27.3 t/s | 27.3 tok/s | Q4_K_M · 7.3 GB | |
| 4 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 5.0 GB | |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 144 t/s | 144 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Reasoning Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 26.2 t/s | 26.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 5.0 GB | |
| 3 | Qwen2.5 7B InstructAlibaba Q4_K_M · 5.8 GB · 33.3 t/s | 33.3 tok/s | Q4_K_M · 5.8 GB | |
| 4 | Gemma 2 9B ITGoogle Q4_K_M · 7.3 GB · 27.3 t/s | 27.3 tok/s | overall | Q4_K_M · 7.3 GB |
| 5 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 144 t/s | 144 tok/s | Q4_K_M · 5.5 GB |
| # | Model | Speed | Long context Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 51.0 t/s | 51.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 26.2 t/s | 26.2 tok/s | Q4_K_M · 6.6 GB | |
| 3 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 144 t/s | 144 tok/s | Q4_K_M · 5.5 GB | |
| 4 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 31.3 t/s | 31.3 tok/s | Q4_K_M · 5.6 GB | |
| 5 | Spark X2.6 4BXHToken Q4_K_M · 4.3 GB · 56.2 t/s | 56.2 tok/s | Q4_K_M · 4.3 GB |
| # | Model | Speed ↓ | llmrun Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 4BAlibabaHow to run → Q4_K_M · 3.4 GB · 51.0 t/s | 51.0 tok/s | Q4_K_M · 3.4 GB | |
| 2 | Ling 3.0 TinyInclusion AI Q4_K_M · 5.5 GB · 144 t/s | 144 tok/s | Q4_K_M · 5.5 GB | |
| 3 | Gemma 4 E4B ITGoogle Q4_K_M · 5.6 GB · 31.3 t/s | 31.3 tok/s | Q4_K_M · 5.6 GB | |
| 4 | Q4_K_M · 6.5 GB · 26.8 t/s | 26.8 tok/s | Q4_K_M · 6.5 GB | |
| 5 | Qwen3.5 9BAlibaba Q4_K_M · 6.6 GB · 26.2 t/s | 26.2 tok/s | Q4_K_M · 6.6 GB |
| # | Model | Speed | Vision Score | Quant · VRAM |
|---|---|---|---|---|
| 1 | Qwen3.5 9BAlibabaHow to run → Q4_K_M · 6.6 GB · 26.2 t/s | 26.2 tok/s | Q4_K_M · 6.6 GB | |
| 2 | Qwen3.5 4BAlibaba Q4_K_M · 3.4 GB · 51.0 t/s | 51.0 tok/s | Q4_K_M · 3.4 GB | |
| 3 | Gemma 3 4B ITGoogle Q4_K_M · 5.0 GB · 58.6 t/s | 58.6 tok/s | Q4_K_M · 5.0 GB | |
| 4 | Q4_K_M · 6.5 GB · 26.8 t/s | 26.8 tok/s | Q4_K_M · 6.5 GB | |
| 5 | MiniCPM V 4.7 1BOpenBMB Q4_K_M · 1.6 GB · 130 t/s | 130 tok/s | Q4_K_M · 1.6 GB |
The GTX 1070 is an entry card in the 8 GB class: it runs 7–9B models well. It is power-hungry for its speed, and the best speed per dollar of the cards compared at ≈$90 used. Pascal generation, released 2016.
GTX 1070 strengths and weaknesses
Strengths
- Runs 7–9B models: Qwen3.5 9B at 26.2 tok/s in 6.6 GB
- Low power draw: 150 W board power; a 300 W PSU is enough
Weaknesses
- 14B models don't fit: Qwen3 14B needs 11.9 GB at Q4 vs 8 GB here
- Little room for context: Qwen3.5 9B uses 6.6 of 8 GB, 1.4 GB left
- Slow prompt reading: A 4K-token prompt on Qwen3.5 9B waits 27.1 s for the first token
- No FP8 or NVFP4: Pascal predates both; use GGUF, AWQ or GPTQ builds
Speed per dollar and per watt
On Qwen3.5 9B (9B params) at Q4_K_M. New prices are compared with new, used with used. The last two columns are the speed you get per $100 of price and per 100 W of board power; higher is better. The GTX 1070 has no new price, so the New view shows it at its MSRP.
| Card | New price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
RTX 50608 GBBest new value | about $460 | 45.8 | 10.0 | 31.6 |
RTX 507012 GB | about $840 | 68.7 | 8.2 | 27.5 |
GTX 10708 GB | about $379 | 26.2 | 6.9 | 17.5 |
RTX 40608 GB | about $520 | 27.8 | 5.3 | 24.2 |
| Card | Used price | tok/s | tok/s per $100 | tok/s per 100 W |
|---|---|---|---|---|
GTX 10708 GB | about $90 | 26.2 | 29.1 | 17.5 |
RTX 3060 12GB12 GB | about $329 | 36.8 | 11.2 | 21.6 |
RTX 50608 GBBest used value | about $430 | 45.8 | 10.7 | 31.6 |
RTX 407012 GB | about $575 | 51.5 | 9.0 | 25.8 |
RTX 40608 GB | about $335 | 27.8 | 8.3 | 24.2 |
RTX 4070 SUPER12 GB | about $675 | 51.5 | 7.6 | 23.4 |
RTX 4060 Ti 8GB8 GB | about $400 | 29.4 | 7.4 | 18.4 |
RTX 4070 Ti12 GB | about $722 | 51.5 | 7.1 | 18.1 |
Cost per token: the GTX 1070 against 8–12 GB cards
On Qwen3.5 9B (9B params, Q4_K_M, 4K context). Speed, cost per token and power per token depend on the model.
- Card, used price
- $0.22
- Power
- $0.24
Renting costs as much as buying after ≈500 hours (≈0.3 years at 4 h a day).
Each dot is a card; hover or tap it for its numbers, click to open it. Cards without a price for the chosen basis are left out. How we calculate cost per token
Cards people weigh against the GTX 1070, each with the one reason it is worth a look. Speeds on Qwen3.5 9B.
Output speed depends on memory bandwidth (256 GB/s). Grey rows need more than 8 GB.
- 9BQwen3.5 9B · Q4_K_M · 6.6 GB26.2tok/s
- 14BQwen3 14B · not at 4-bitneeds 11.9 GB
- 27–31BQwen3.8 27B · not at 4-bitneeds 17.9 GB
- 35B MoEQwen3.6 35B A3B · not at 4-bitneeds 22.0 GB
- 70BLlama 3.3 70B Instruct · not at 4-bitneeds 51.3 GB
- 120B MoEGPT OSS 120B · not at 4-bitneeds 70.8 GB
Estimated speeds: Q4_K_M, 4K context, one user. How we estimate
All 842 models on the GTX 1070
Featured onlyBest quant that fits, ordered by llmrun Score. Fit grades how well it runs here, S best. How ranking works
| Model | Quant | VRAM | Speed | Context | Score | Fit |
|---|---|---|---|---|---|---|
Nanonets OCR SVision | Q4_K_M | 3.2 GB | 63.3 t/s | 128K | A84, Runs well | |
| BF16 | 3 GB | 55.5 t/s | 2K | A82, Runs well | ||
| Q3_K_M | 7.7 GB | 24.2 t/s | 131K | C40, Usable | ||
Qwen3 8B 64k Context 2X Josiefied UncensoredChatReasoningFunctions | Q4_K_M | 7.6 GB | 30.1 t/s | 66K | B48, Decent | |
MiMo 7B RLChatFunctions | Q4_K_M | 7.4 GB | 31.4 t/s | 33K | B52, Decent | |
Opt 350MChat | FP16 | 0.8 GB | 216.1 t/s | 2K | S100, Runs great | |
Mamba 1.4B HFChat | Q4_K_M | 2.5 GB | 182.9 t/s | — | S99, Runs great | |
TinyStories 1MChat | BF16 | 0.0 GB | 16640.0 t/s | 2K | S100, Runs great | |
Granite 3.3 2B InstructChatFunctions | Q4_K_M | 3.2 GB | 83.6 t/s | 131K | S88, Runs great | |
LFM2 8B A1BChat | Q4_K_M | 5.5 GB | 135.5 t/s | 128K | S96, Runs great | |
Gpt2 MediumChat | Q4_K_M | 0.3 GB | 665.6 t/s | 1K | S100, Runs great | |
LFM2.5 230M BaseChat | Q4_K_M | 0.6 GB | 332.8 t/s | 128K | S100, Runs great | |
Dobby Mini Unhinged Llama 3.1 8BChatRoleplay | Q4_K_M | 7.3 GB | 30.9 t/s | 131K | B56, Decent | |
| Q4_K_M | 7.7 GB | 32.2 t/s | 33K | B47, Decent | ||
| Q4_K_M | 6.0 GB | 27.5 t/s | 131K | A68, Runs well |
Specs
- Architecture
- Pascal
- VRAM
- 8 GB GDDR5
- Memory bandwidth
- 256.0 GB/s
- FP16 (dense)
- 6.46 TFLOPS
- CUDA cores
- 1,920
- Tensor cores
- 0
- Compute capability
- 6.1
- TDP
- 150 W
- Released
- 2016-06-10
- MSRP
- $379
GTX 1070 FAQ
Is the GTX 1070 still worth it in 2026?
At ≈$90 used you get 29.1 tok/s per $100 on Qwen3.5 9B, which ranks 1st of the 8 cards compared here; it is also the 16th fastest out of 19 cards with 8 GB. It runs 7–9B models well; 14B doesn't fit in 8 GB.
What is the largest model the GTX 1070 can run?
Qwen3.5 9B (9.7B) is the largest at 4-bit or better: at Q4_K_M it uses 6.6 GB of 8 GB, and runs at about 26.2 tok/s.
Can the GTX 1070 run 14B models?
No. Qwen3 14B needs 11.9 GB at Q4, more than the 8 GB on this card.
How many tokens per second does the GTX 1070 give?
About 26.2 tok/s on Qwen3.5 9B at Q4_K_M, with the first token after 27.2 s on a 4K-token prompt.
What power supply does the GTX 1070 need?
Plan a 300 W PSU (2× the 150 W TDP).
Is renting cheaper than buying a GTX 1070?
An RTX 3090 (24 GB) rents from $0.18/hr. Renting costs as much as buying after ≈500 hours (≈0.3 years at 4 h a day).