Gemma 4 E4B
Local OKExcellentA · 44
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 90 tok/s, typical error 5% · first token ~0.4s
Fits in 8 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 3070.
Best Local AI Models for RTX 3070 (8GB)
The RTX 3070 streams memory faster than both the RTX 3060 and the RTX 4060 (448 GB/s against 360 and 272), so an 8B model runs at est. 61 tok/s, typical error 5%. That is 22% faster than the 3060. The limit is capacity: 8GB of VRAM holds one 7B-8B model at Q4 with little room for context, where the 3060 offers 12GB. For a card bought for local LLMs, a 12GB or 16GB model is the better buy; the 3070 makes sense if you already own one.
Quick answer
The best local LLM for the RTX 3070 is Gemma 4 E4B (est. 90 tok/s, typical error 5%) on its 8GB VRAM. It uses ~4GB of VRAM; the RTX 3070 handles up to 8B parameter models at Q4. A 14B model at Q4 with CPU offload runs at est. 7 tok/s, typical error 15%. (ModelFit, 2026)
Sizing rule: a Q4 model needs about 0.6 GB of VRAM per billion parameters, and ModelFit budgets 90% of this card's 8GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Qwen3.5 4B Instruct (Q8) (4B, ~4.3GB) and LFM2.5 8B-A1B (8.3B, ~5.5GB). On this card Gemma 4 E4B runs at est. 90 tok/s, typical error 5%. What it will not run: a 14B model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run gemma4:e4b
- Top pick
- Gemma 4 E4B
- Decode speed
- est. 90 tok/s, typical error 5%
- VRAM needed
- ~4 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks. Typical error: the median gap between the ModelFit speed model and measured runs on hardware the model was not fitted on, for this backend and model size.
Cite this page: ModelFit, RTX 3070 8GB for Local LLMs (2026): 7B-8B Only, est. 61 tok/s, https://modelfit.io/gpu/rtx-3070/, updated October 2026, CC BY 4.0.
Specs
VRAM
8 GB GDDR6
Speed (8B Q4)
est. 61 tok/stypical error 5%
Bandwidth
448 GB/s
Architecture
Ampere
Max model size
Up to 8B parameter models
Compatibility
10 excellent, 0 workable
Vendor
Table 1
RTX 3070 estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 8GB |
|---|---|---|
| 7B | est. 69 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 7 tok/stypical error 15% | CPU offload (slow) |
| 20B MoE (3.6B active) | est. 28 tok/stypical error 15% | CPU offload (slow) |
| 32B | est. 2 tok/stypical error 5% | CPU offload (slow) |
| 35B MoE (3B active) | est. 33 tok/stypical error 15% | CPU offload (slow) |
| 70B | est. 1 tok/stypical error 10% | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 21 tok/stypical error 15% | CPU offload (slow) |
Fig. 1RTX 3070: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 448 GB/s, typical error 5-15%
Fits in 8 GBCPU offload (slow)
7B69
14B7
20B MoE (3.6B active)28
32B2
35B MoE (3B active)33
70B1
120B MoE (5.1B active)21
tok/s est.ModelFit estimates, not measured benchmarks: decode speed of a generic Q4_K_M model of each size at 4K context on the RTX 3070's 448 GB/s bandwidth. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 8GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. What typical error means.
Fig. 2
Context costs VRAM too.
Gemma 4 E4B loads ~4 GB of weights; at 16k context the KV cache adds ~2.0 GB (still fits the ~7 GB usable VRAM), and at 64k it adds ~8.0 GB (exceeds the budget, use a smaller quant or a q8_0 KV cache).
Weights 4.0 GBKV cache · 16k context 2.0 GBHeadroom 1.2 GBRuntime reserve 0.8 GB
The model leaves about 1.2 GB of the usable VRAM budget free after weights and 16k context.
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Get told when a model beats Gemma 4 E4B on your RTX 3070
One email when a new open-weight model is a better fit for 8 GB of VRAM, plus the Thursday weekly.
You'll also get the weekly ModelFit email. By signing up you agree to our Privacy Policy. Unsubscribe from either email anytime, in one click.
Where to buy the RTX 3070
Storage & accessories for your model library
- Internal NVMe SSD · 2TBA Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.Check price on Amazon
- USB4 NVMe Enclosure40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.Check price on Amazon
ModelFit may earn a commission on purchases through these links, at no extra cost to you.
RTX 3070 VRAM for AI: what actually fits?
Of the 8GB of GDDR6, the engine budgets about 7.2GB for weights and context. Qwen3 8B at Q4 takes about 6.5GB, which leaves roughly 0.7GB for the KV cache, so long contexts are the first thing to give. Qwen 2.5 7B and Mistral 7B (about 5.5GB each) leave more room. Bandwidth is the strength: 448 GB/s is above both the RTX 3060 (360) and the RTX 4060 (272), and token generation is bandwidth-bound, which is why the 3070 reaches est. 61 tok/s, typical error 5% against est. 50 tok/s on the 3060. The weakness is the 8GB ceiling it shares with the 4060: a 12B model such as Gemma 4 12B (8GB) is over budget, and a 14B model spills into system RAM, where the estimate falls to est. 7 tok/s, typical error 15%. A card meant for local LLMs is better served by 12GB (RTX 3060) or 16GB (RTX 4060 Ti, RTX 5060 Ti).
Table 2
What does not fit in 8GB (and what it costs you)
With 8GB, the honest ceiling is a single 7B model at Q4. Anything larger does not simply run slower: the layers that do not fit spill to system RAM over PCIe, and decode speed falls off a cliff. These are the same bandwidth-derived estimates used in the table above, shown here as the penalty rather than the headline.
| Model size | Fits in 8GB? | Est. speed | Slowdown vs 7B |
|---|---|---|---|
| 7B | Yes | est. 69 tok/stypical error 5% | n/a |
| 14B | No, needs ~2GB more in system RAM | est. 7 tok/stypical error 15% | ~10x slower |
| 20B MoE (3.6B active) | No, needs ~5GB more in system RAM | est. 28 tok/stypical error 15% | ~2x slower |
| 32B | No, needs ~12GB more in system RAM | est. 2 tok/stypical error 5% | ~35x slower |
| 35B MoE (3B active) | No, needs ~14GB more in system RAM | est. 33 tok/stypical error 15% | ~2x slower |
Mixture-of-experts models are the exception worth knowing: they activate only a fraction of their parameters per token, so a large MoE can stay usable on a small card where a dense model of the same total size will not. Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
Table 3
RTX 3070 vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 4060 | 8 GB | est. 40 tok/stypical error 5% | 272 GB/s |
| RTX 3070 | 8 GB | est. 61 tok/stypical error 5% | 448 GB/s |
| RTX 3060 | 12 GB | est. 50 tok/stypical error 5% | 360 GB/s |
| RTX 4060 Ti | 16 GB | est. 42 tok/stypical error 5% | 288 GB/s |
| RTX 5060 Ti | 16 GB | est. 68 tok/stypical error 5% | 448 GB/s |
Recommended models
registry-verified10 modelsQwen3.5 4B Instruct (Q8)
Local OKExcellentA · 40
Best for: Coding, Agents, Multimodal·Pop: 88/100
Perf: est. 71 tok/s, typical error 5% · first token ~1.2s
Fits in 8 GB VRAM with room to spare. Best for coding, agents, multimodal on RTX 3070.
LFM2.5 8B-A1B
Local OKOKB · 24
Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100
Perf: est. 164 tok/s, typical error 15% · first token ~0.1s
Fits in 8 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 3070.
Ornith 1.0 9B
Local OKOKB · 22
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 65 tok/s, typical error 5% · first token ~0.4s
Fits in 8 GB VRAM with room to spare. Best for agentic coding on small machines on RTX 3070.
Ministral 3 8B
Local OKOKB · 17
Best for: Chat, Coding, Vision·Pop: 74/100
Perf: est. 54 tok/s, typical error 5% · first token ~0.5s
Fits in 8 GB VRAM with room to spare. Best for chat, coding, vision on RTX 3070.
Gemma 3 4B Instruct (Q8)
Local OKExcellentA · 46
Best for: Chat, Coding·Pop: 81/100
Perf: est. 74 tok/s, typical error 5% · first token ~1.2s
Fits in 8 GB VRAM with room to spare. Best for chat, coding on RTX 3070.
Gemma 4 E2B (Q8)
Local OKExcellentA · 36
Best for: IoT, Mobile, Edge·Pop: 76/100
Perf: est. 138 tok/s, typical error 5% · first token ~1.2s
Fits in 8 GB VRAM with room to spare. Best for iot, mobile, edge on RTX 3070.
MiniCPM-V 4.5 8B
Local OKOKB · 21
Best for: Vision, Multimodal·Pop: 74/100
Perf: est. 63 tok/s, typical error 5% · first token ~0.4s
Fits in 8 GB VRAM with room to spare. Best for vision, multimodal on RTX 3070.
Phi-4 Mini 3.8B (Q8)
Local OKExcellentA · 47
Best for: Coding, Chat·Pop: 75/100
Perf: est. 70 tok/s, typical error 5% · first token ~1.3s
Fits in 8 GB VRAM with room to spare. Best for coding, chat on RTX 3070.
Qwen2.5 Coder 7B
Local OKOKB · 24
Best for: Coding·Pop: 72/100
Perf: est. 71 tok/s, typical error 5% · first token ~0.4s
Fits in 8 GB VRAM with room to spare. Best for coding on RTX 3070.
Similar GPUs for local AI
- RTX 306012GB · est. 50 tok/s
- RTX 40608GB · est. 40 tok/s
- RTX 4060 Ti16GB · est. 42 tok/s
- RTX 5060 Ti16GB · est. 68 tok/s
Speeds are estimates for Qwen3 8B Q4_K_M, typical error 5%.
Compatible model families
- QwenAlibaba Cloud: Qwen3.8 27B local flagship (Apache 2.0, vision)
- LlamaMeta: Most popular open-weight model family
- DeepSeekDeepSeek AI: V4.1 Flash: multimodal 552B MoE, API only
- MistralMistral AI: Excellent performance-per-parameter ratio
- GemmaGoogle DeepMind: Gemma 4 E4B: 4.5B effective, runs on 8GB Macs
- PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 3070 FAQ: common questions
How much VRAM does the RTX 3070 have for LLMs?
The RTX 3070 has 8GB of GDDR6 VRAM on a 448 GB/s memory system. The engine budgets about 7.2GB of that for model weights and context, enough for a single 7B-8B model at Q4 quantization with little room left for a long context window.
What size LLM can I run on an RTX 3070?
Up to 8B parameters at Q4 quantization. Qwen3 8B takes about 6.5GB, while Qwen 2.5 7B and Mistral 7B take about 5.5GB each. A 12B model such as Gemma 4 12B (8GB) is over the budget, and 14B models spill into system RAM, where the estimate falls to est. 7 tok/s, typical error 15%.
Is the RTX 3070 good for local AI in 2026?
For 7B-8B models, yes: it reaches est. 61 tok/s, typical error 5%, fast enough for interactive chat. Its limit is capacity, not speed. If you are choosing a card for local LLMs, a 12GB or 16GB model is the better buy because it keeps larger models and longer context fully in VRAM. Keep a 3070 if you already own one; do not pick one for LLMs alone.
RTX 3070 vs RTX 3060 for running LLMs?
The 3070 is faster on models that fit: 448 vs 360 GB/s of memory bandwidth gives est. 61 tok/s, typical error 5% against est. 50 tok/s, about 22% more. The 3060 has 12GB against 8GB, so it holds models and context the 3070 cannot. For 7B-8B models only, the 3070 is the quicker card; for room to grow, the 3060 12GB or a 16GB card is the safer pick.
RTX 3070 vs RTX 4060 for local AI?
Both have 8GB, so they run the same models. The 3070 streams 448 GB/s against the 4060's 272 GB/s, which makes it 52% faster on 8B models (est. 61 tok/s, typical error 5% vs est. 40 tok/s). The 4060 is the newer Ada Lovelace card, but decode speed at this size follows memory bandwidth, so the 3070 is the faster of the two.
How fast is a 27B-class model on the RTX 3070?
The RTX 3070's 8GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 7B, at est. 69 tok/s, typical error 5%. The largest individual model in the catalog that fits is Ornith 1.0 9B (9B).
Related guides & benchmarks
- Best AI Models for 8GB VRAM (2026)Ranked 3B-8B picks for 8GB cards like the RTX 3070, and why 12B does not fit.
- How Much VRAM Do You Need for Local LLMs?A tier-by-tier table from 8GB to 96GB: what each size of card can run.
- Gemma 4 GPU Requirements: Every VariantWhich Gemma 4 variants fit an 8GB GPU, and which need 12GB or more.
Sizing local AI? Start with RAM & VRAM
Browse all NVIDIA GPUs for AI
Real-world calibration
Have this hardware? Share a measured run.
Measure a fixed Ollama reference model on your machine. This separate, opt-in contribution does not change ModelFit's third-party SWE-Bench leaderboard; results appear on the measured-speed leaderboard after the next site update.
$ npx -y @wecko-ai/modelfit bench --submit --cleanup
Want personalized recommendations?
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.