TL;DR: The used RTX 4070 12GB beats the new RTX 5060 8GB for local AI on both metrics that matter — 504 GB/s vs 448 GB/s bandwidth and 12GB vs 8GB of VRAM. The catch is price: the 5060 is a ~$339 new card, the 4070 is a ~$450 used one. This is not a same-price fight, so the answer depends on whether your ceiling is $350 or $500.

	New RTX 5060 8GB	Used RTX 4070 12GB	Used RTX 3090 24GB
Best for	Strict sub-$350 budget, 7–8B chat	Best sub-$500 local-AI card	Max VRAM, 30B+ and 70B offload
Typical price (Jul 2026)	~$329–$349 new	~$430–$470 used	~$1,000–$1,100 used
VRAM	8 GB	12 GB	24 GB
Memory bandwidth	448 GB/s	504 GB/s	936 GB/s
Largest model that fits well	8B Q4	14B Q4	32B Q4 / 70B partial
TDP	145 W	200 W	350 W

Honest take: If your absolute cap is $350, buy the RTX 5060 8GB and be happy — it runs 8B models at interactive speed. But if you can stretch to ~$450, the used RTX 4070 12GB is the materially better local-AI card: it runs 14B models the 5060 physically cannot, and it moves memory ~12% faster on top.

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The spec-sheet correction most comparisons get wrong

There is a persistent myth in local-AI buying threads that the RTX 5060’s memory bandwidth is roughly half the RTX 4070’s. That is wrong, and it usually comes from confusing the 5060 with the older RTX 4060.

Here are the real numbers, straight from the spec sheets:

Spec	RTX 5060 8GB	RTX 4070 12GB
Memory type	GDDR7	GDDR6X
Bus width	128-bit	192-bit
Memory speed	28 Gbps	21 Gbps
Bandwidth	448 GB/s	504 GB/s
VRAM	8 GB	12 GB
Interface	PCIe 5.0 ×8	PCIe 4.0 ×16

The RTX 5060 uses fast GDDR7 (28 Gbps) on a narrow 128-bit bus. The RTX 4070 uses slower GDDR6X (21 Gbps) but a wider 192-bit bus. Bus width wins: 504 GB/s vs 448 GB/s, a 12.5% bandwidth advantage for the 4070.

That’s the important framing. The 4070 is faster, but it is not twice as fast. Anyone who dismisses the 5060 as a memory cripple is repeating the RTX 4060’s 272 GB/s number by mistake. For LLM inference — which is almost entirely memory-bandwidth-bound during token generation — a 12.5% bandwidth gap translates to a roughly 12.5% tok/s gap on models that fit both cards. Meaningful, but not decisive on its own.

What is decisive is the VRAM difference, and that only shows up once you try to load a model bigger than 8GB.

The price reality in July 2026 (why “under $350” only describes one of these cards)

The queue premise for this comparison assumed both cards land at a similar $280–$320 street price. In July 2026, that is no longer true, and it changes the whole decision.

• RTX 5060 8GB: $299 MSRP at launch (May 2026), currently selling around $329–$349 for AIB models — ~$339 is a fair typical figure on Amazon and at Micro Center. It’s a new card with a warranty.
• RTX 4070 12GB: discontinued as a new product, so the relevant price is the used market. Used 4070s are running around $450 on eBay as of early July 2026 (the RTX 4070 Super sits a bit higher near ~$549, the 4070 Ti near ~$590).

We’re in the middle of the 2026 memory-price surge — GDDR and DDR5 costs have climbed sharply as fabs reallocate capacity to HBM for AI datacenters — which is exactly why a two-year-old used 4070 hasn’t collapsed in price the way a used mid-range card normally would. (We covered the mechanics of that surge in DDR5 and SSD prices doubled in 2026.)

So the real question isn’t “which $300 card is better.” It’s: is the extra ~$110 for a used 4070 worth it for local AI? For most people who run local LLMs seriously, the answer is yes — and here’s the data behind that.

What each card can actually run

LLM inference obeys one rule above all others: if the model fits entirely in VRAM, it runs fast; if it spills into system RAM, tokens per second collapse to single digits.

Here’s approximate VRAM needed at Q4_K_M including KV cache at 8K context:

Model	Approx. VRAM	Fits RTX 5060 8GB?	Fits RTX 4070 12GB?
Mistral 7B v0.3	~5.0 GB	✅ Yes	✅ Yes
Llama 3.1 8B	~5.5 GB	✅ Yes (tight headroom)	✅ Yes
Qwen3 8B	~5.6 GB	✅ Yes	✅ Yes
Gemma 3 9B	~6.5 GB	✅ Barely	✅ Yes
Phi-4 14B	~8.7 GB	❌ No	✅ Yes
Qwen3 14B	~9.0 GB	❌ No	✅ Yes (tight)
Any 30B dense	16 GB+	❌ No	❌ No

This is the whole ballgame. The RTX 5060’s 8GB ceiling locks you to the 7B–9B tier. On 8GB you want to stay under ~6.5GB loaded to leave room for context and system overhead; push past 7.5GB and you’ll start hitting out-of-memory errors on longer conversations.

The RTX 4070’s 12GB clears that wall. A 14B model at Q4_K_M — Phi-4, Qwen3 14B, and their peers — fits, with enough left for a usable 8K–16K context. For a lot of home-lab users, 14B is the sweet spot where a local model stops feeling like a toy and starts being genuinely useful for coding help, summarization, and structured extraction.

Neither card touches 30B dense models. If that’s your target, you’re in used RTX 3090 24GB territory, and no amount of quantization changes that on 8–12GB.

Tokens per second: the head-to-head

Here’s what the two cards actually deliver. These are community-measured Ollama figures at Q4_K_M; llama.cpp runs ~3–10% faster than Ollama because it skips the Go server overhead.

Model / quant	RTX 5060 8GB	RTX 4070 12GB
Llama 3.1 8B Q4_K_M	~25–35 tok/s	~35–45 tok/s
Mistral 7B Q4_K_M	~30–35 tok/s	~40–50 tok/s
Qwen3 14B Q4_K_M	❌ won’t fit	~33–42 tok/s

Two things stand out.

First, on the 8B models both cards run, the 4070’s lead is roughly what the bandwidth ratio predicts — call it 30 tok/s vs 40 tok/s, about a third faster in practice once you account for its higher clocks and wider bus. Both are comfortably above reading speed (~7–10 tok/s), so 8B chat feels fast on either card.

Second, the 14B row is the one that matters. The RTX 5060 doesn’t post a slow number there — it posts no number, because a 14B Q4 model exceeds 8GB before a single token is generated. When you force it, Ollama offloads overflow layers to system RAM and throughput drops to 1–4 tok/s, slower than you can read. The 4070 runs the same model at a usable 33–42 tok/s.

That’s the practical difference between these cards: not “faster,” but “runs a class of model the other one can’t.”

For reference, the RTX 5060 Ti 16GB — which shares the 5060’s exact 448 GB/s memory config — runs Qwen3 14B at Q8 around ~50 tok/s, which is what more VRAM buys you at the same bandwidth. We break the whole Blackwell lineup down in Best local LLM for every RTX 50-series GPU.

Bandwidth vs VRAM: which one should decide your buy?

The clean mental model, which we lean on across the site:

VRAM decides what you can run. Bandwidth decides how fast it runs once it fits.

The RTX 4070 wins both here, but they’re not equally important. A 12.5% bandwidth edge is nice-to-have. The extra 4GB of VRAM is the thing that unlocks an entirely new model tier. If the two cards were the same price, this wouldn’t even be a discussion — you’d take the 4070 every time.

The only reason the 5060 is in the conversation at all is price and warranty: it’s a new card at ~$339 versus a used card at ~$450. If your budget is a hard $350, the 4070 simply isn’t an option and the 5060 8GB is a perfectly good 8B runner. If you have ~$450–$500, the 4070’s extra headroom is worth the money for anyone doing more than casual 7B chat.

Power and efficiency

The 5060 is the more efficient card if that matters for a 24/7 always-on rig:

• RTX 5060 8GB: 145 W TDP. At $0.12/kWh, a card pulling ~130W under sustained inference costs about $0.016/hour, or roughly $11–12/month if it ran flat-out 24/7 (real always-on rigs idle most of the time, so expect a few dollars).
• RTX 4070 12GB: 200 W TDP. Under load closer to ~185W, about $0.022/hour, or ~$16/month at a 24/7 flat-out worst case.

The gap is real but small in dollar terms — a few dollars a month. Efficiency alone shouldn’t swing this decision unless you’re running a genuinely always-on server, in which case see our power bill math for a 24/7 AI server.

Who should buy which

Buy the new RTX 5060 8GB if:

• Your budget is a firm $350 or less.
• You mainly run 7B–8B models for chat, autocomplete, and light coding help.
• You want a new card with a full warranty and don’t want to gamble on used-market condition.
• Low power draw / a small quiet build matters (SFF, HTPC-adjacent).

Buy the used RTX 4070 12GB if:

• You can stretch to ~$450–$500.
• You want to run 14B models (Phi-4, Qwen3 14B) at usable speed — the single biggest reason to pick it.
• You want a bit more bandwidth on the 8B models you’ll still run daily.
• You’re comfortable buying used and testing the card within the return window.

Skip both and buy a used RTX 3090 24GB if:

• Your real target is 30B-class models, longer context, image generation with FLUX, or 70B via partial offload.
• You can afford ~$1,000–$1,100. At 936 GB/s and 24GB, it’s still the value king above the $500 tier, and it does everything these two cards can plus a whole tier more.

What if you don’t want to buy hardware at all?

If your local-AI needs are occasional — a burst of 14B inference a few evenings a week — renting can pencil out better than buying either card. A cloud GPU on RunPod lets you spin up a 24GB (or larger) card for the duration of a job and pay only for active time, with no memory-surge markup and no used-market gamble. We walk through the full break-even math in RunPod vs local GPU: rent or buy. The rule of thumb: if you run local models more than about an hour a day, owning wins within a year; below that, renting is cheaper.

For picking the right model to run on whichever card you land on, our best local AI models by VRAM tier guide maps each VRAM budget to its best model. And if you’re using these cards to back a coding assistant, aicoderscope.com covers the Cursor/Continue/Cline side, while aifoss.dev has the open-source self-hosting setup guides.

FAQ

Is the RTX 5060 really only half the bandwidth of the RTX 4070? No. That’s a common mix-up with the older RTX 4060 (272 GB/s). The RTX 5060 runs 448 GB/s of GDDR7; the RTX 4070 runs 504 GB/s of GDDR6X. The 4070 is ~12.5% faster, not 2× faster.

Can the RTX 5060 8GB run a 14B model at all? Not usefully. A 14B model at Q4_K_M needs ~8.7–9GB just for weights, which exceeds the card’s 8GB total. Forcing it offloads layers to system RAM and drops speed to 1–4 tok/s. Stick to 7B–9B on 8GB.

Why is a used two-year-old RTX 4070 still ~$450? The 2026 memory-price surge. Fabs are prioritizing HBM for AI datacenters, which has pushed up GDDR and DDR5 prices and kept used mid-range GPUs from depreciating the way they normally would.

Is the RTX 4070 Super worth the extra ~$100 over a base 4070? For local AI, not really — both are 12GB/504 GB/s cards, so they run the same models at nearly the same tok/s. The Super’s extra CUDA cores help gaming more than bandwidth-bound inference. If you’re paying used-market prices, the cheaper base 4070 is the better local-AI value.

Which is better for image generation (Stable Diffusion / FLUX)? The RTX 4070’s 12GB is meaningfully more comfortable. SDXL is tight on 8GB and FLUX.1 dev essentially needs more than 8GB for a smooth workflow. If image gen is on your list, the 4070 (or a 3090) is the pick.

Sources

1. NVIDIA — GeForce RTX 5060 Family specifications (448 GB/s, GDDR7, 8GB, 128-bit).
2. ASUS — Prime GeForce RTX 4070 12GB GDDR6X tech specs (504 GB/s, 192-bit, 21 Gbps).
3. Best Value GPU — RTX 4070 price tracker (Jul 2026) and RTX 5060 price tracker.
4. Micro Center — RTX 5060 8GB GDDR7 listings (current ~$329–$349 street pricing).
5. Databasemart — RTX 5060 Ollama GPU benchmarks (measured 7B–8B tok/s on 8GB).
6. Compute Market — RTX 5060 for Local AI 2026 review ($299, 8GB VRAM analysis).
7. ModelFit — RTX 4070 Super local LLM tok/s (7B–9B ~56 tok/s reference for the 12GB/504 GB/s class).

Recommended Gear

• RTX 5060 8GB — new sub-$350 card for 7B–8B local LLMs.
• RTX 4070 12GB — best sub-$500 local-AI GPU; runs 14B models the 5060 can’t.
• RTX 3090 24GB — the value king above $500 for 30B+ models and image generation.

Prices as of July 2026 and move weekly — verify current retailer and eBay listings before buying.