For someone starting deep learning on an RTX 5070, the question in late 2026 isn’t performance butLoad capacityis the answer. Compute (6,144 CUDA, 5th-gen tensor) is plenty, and 672 GB/s bandwidth is 33% faster than the 4070 Super (504). But what actually stops your work is the wall that 12GB of VRAM builds. Here I answer ”how far does it go” with arithmetic, not vibes.
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1. Bottom line first: 24B doesn’t fit on a 5070
By llama.cpp-style Q4_K_M conventions, weight size is roughly 0.61GB per 1B parameters. On Windows, after subtracting CUDA overhead, the actually usable VRAM is 88% of 12GB, about 10.6GB.
| Model (Q4_K_M) | Weights | KV cache headroom | Verdict |
|---|---|---|---|
| Qwen3-8B (4.9GB) | 4.9GB | ~5.7GB | Comfortable even for long context |
| Gemma 3 12B (7.3GB) | 7.3GB | ~3.3GB (tens of thousands of tokens) | Runs comfortably |
| Mistral Small 3 (24B, 14.6GB) | 14.6GB | – | Offload required |
| Qwen3-30B-A3B (MoE) | Only 3B active, but a structure with many resident weights | – | Layer offload, conditional (~12 tok/s) |
The crux here is the 24B row. Overseas community benchmarks also classify Mistral Small Q4 as a “tight fit,” and real token speed drops to 28 tok/s. In other words, the 5070’s limit isn’t “a card where 24B gets slow” butCards that can’t hold 24Bis.
2. Bandwidth sets inference speed, and the 5070 is honest about that line
Decoding (token generation) scales with memory bandwidth, not compute. The speedup you can expect from a card swap, computed from bandwidth alone:
- RTX 5070 (672 GB/s): measured 12B Q4 benchmark ~45 tok/s
- RTX 5060 Ti 16GB (448 GB/s): same model ≈ 30 tok/s (45×448/672)
- RTX 4090 (1008 GB/s): ≈ 67 tok/s + 24GB VRAM for 24B Q4-native
The 5070 is a “fast but shallow” card; the 5060 Ti 16GB is a “slow but deep” card. And in deep-learning use, if depth is shallow, the chance to use the fast speed itself disappears.
3. Convert to monthly cost and the answer changes
As of May 2026, domestic 5070 street price runs in the 100~₩1.1M range, and the 5060 Ti 16GB in the low ₩900,000 range (Namuwiki RTX 50 article, Danawa basis). Amortized over 36 months + 8 hours/day of inference, electricity at ₩250/kWh:
- 5070 (₩1.05M, 250W): amortization ₩29,167 + electricity ₩15,000 =44,200 KRW/month
- 5060 Ti 16GB (920,000 won, 180W): depreciation 25,556 won + electricity 10,800 won =36,400 won/month
Converted to per-token cost at 12B Q4: the 5070 comes to about ₩457 per 100K tokens, the 5060 Ti 16GB about ₩274. When running the same modelthe 5060 Ti side is 1.67x cheaperat all. Including the 24B that can’t run on a 5070 anyway widens the gap even further.
4. When you still buy the 5070 / when you shouldn’t
If you’re considering a laptop GPU, read first about the issue that the same name performs differently depending on TGP:Laptop RTX GPU TGP, Fully Mastered.
When buying makes sense: 1440p gaming is the main use and AI is a side feature up to 12B. And at current prices the gap between the two cards is only ₩130~180K, so for “1080p gaming + occasional LLM” the 5070 wins on versatility.
When not to buy: if the goal is training, fine-tuning, or 24B+ inference, neither card is the right answer. The logical alternatives at this price point are (1) a used RTX 3090 24GB — 24GB physically fits in the same slot — and (2) a 5070 Ti Super 16GB — the second-gen Super lineup refresh that, per Quasarzone, dropped 300,000 won within 6 months of launch. Pay more or wait, but don’t pay 1,000,000 won for a 12GB card and expect it to play a 24GB role.
5. Buying-decision checklist
VRAM requirements by use case areVRAM reference table by use case for deep-learning PCsCheck again at
- Does the target model fit under 7.5GB at Q4_K_M? → YES: 5070 is fine / NO: reconsider the VRAM card
- Will you use context above 32K? → Watch the 5.7GB KV cache slot (the 12B combo is at its limit)
- Fine-tuning (LoRA)? → 12GB caps out at 7B LoRA — go 24GB (3090/4090)
False-verdict condition
This post’s logic gets validated once next year.If a used RTX 3090 can’t fall below ₩900,000 by mid-2027The “if it’s the 5070 price range, rather a 3090” claim is wrong. If in the meantime the 5070 drops below 950K KRW, that becomes a refutation of “buy a 5070 now” — keep watching one of the two prices.
Sources: TechPowerUp GPU DB · MSI official specs (6,144 CUDA / 672 GB/s / 250W), everylocalai.com bench aggregation (Qwen3-8B ~65, Gemma3-12B ~45, Mistral Small ~28 tok/s), Namuwiki RTX 50 docs (2026-05: 5070 at ₩1M~1.1M, 5060 Ti 16GB in the ₩900K range), Quasar Zone (report of a 5070 Ti Super price drop). The electricity assumption of ₩250/kWh amortized over 36 months is my own calculation. Prices and specs change — check the latest info before buying.
