The RTX 4070 is not worth double the money if your only goal is running Qwen2.5 14B — both cards expose the same 12 GB ceiling, so they load the same quantization. The 4070 buys speed, not capability: 504 GB/s of memory bandwidth against the RTX 3060's 360 GB/s per TechPowerUp, roughly 1.4x on token generation.
Why two 12 GB cards are not the same card
There is a specific moment in every local-LLM build where the spec sheet stops helping. You have decided on a model — Qwen2.5 14B, a dense 14.7-billion-parameter instruct model that has become the default "serious but single-GPU" choice — and you have narrowed the hardware to two cards that both advertise 12 GB of VRAM. One costs roughly $480 on the street, the other roughly $750. The buffer is identical. The obvious question is whether the extra money buys anything at all.
It does, but not the thing most buyers assume it buys. VRAM capacity is a gate: it decides whether a model runs. Memory bandwidth is a throttle: it decides how fast the model runs once it fits. The RTX 3060 12GB and the RTX 4070 sit on opposite sides of that distinction — same gate, different throttle. Per TechPowerUp's GA106 page the RTX 3060 pairs 12 GB of GDDR6 to a 192-bit bus for 360 GB/s, and per TechPowerUp's AD104 page the RTX 4070 pairs 12 GB of GDDR6X to the same 192-bit bus for 504 GB/s.
This comparison is written for one configuration: a single card, a single user, a local runtime like Ollama or llama.cpp, no tensor parallelism and no second GPU. That is the configuration most people actually build, and it is the one where the bandwidth-versus-capacity distinction is cleanest. If you are planning a multi-GPU box or serving concurrent requests, the arithmetic below still applies but the conclusions shift.
Key takeaways
- Identical VRAM ceiling. Both cards hold 12 GB, so both run exactly the same quantizations of Qwen2.5 14B. Neither unlocks a model the other cannot load.
- ~1.4x memory bandwidth. 504 GB/s versus 360 GB/s per TechPowerUp. Token generation is bandwidth-bound, so this is the number that sets your interactive speed.
- ~2.3x FP32 compute. 29.15 TFLOPS versus 12.74 TFLOPS per TechPowerUp. Prompt processing is compute-bound, so the gap is much wider on long prompts than on short chat turns.
- ~1.6x street price. Roughly $479 versus $749 as of September 2026 on the SpecPicks catalog snapshot below.
- Same power class. 170 W versus 200 W total graphics power — both live inside a quality 550-650 W supply.
Does Qwen2.5 14B actually fit in 12 GB of VRAM?
Yes, at four-bit and five-bit quantization, with room for a useful context window. It does not fit at eight-bit or at full precision.
The model has 14.7 billion parameters per the Qwen2.5-14B-Instruct model card. Quantized file sizes follow from that parameter count and the bits-per-weight of each llama.cpp quantization format; the sizes below match the distributed builds in the Ollama library.
The KV cache is the part buyers forget. Qwen2.5 14B uses grouped-query attention with 48 layers, 8 key-value heads and a head dimension of 128, per the config on the model card. At 16-bit precision that works out to 2 x 48 x 8 x 128 x 2 bytes = 196,608 bytes per token, or roughly 0.19 GB per 1,000 tokens of context. An 8K context therefore costs about 1.5 GB on top of the weights.
| Quantization | Weights | KV cache @ 8K | Total VRAM | Fits in 12 GB? | Quality cost |
|---|---|---|---|---|---|
| q2_K | ~5.8 GB | ~1.5 GB | ~7.3 GB | Yes, easily | Severe — noticeable reasoning degradation |
| q3_K_M | ~7.3 GB | ~1.5 GB | ~8.8 GB | Yes | Noticeable on hard prompts |
| q4_K_M | ~9.0 GB | ~1.5 GB | ~10.5 GB | Yes — the sweet spot | Small, widely considered acceptable |
| q5_K_M | ~10.5 GB | ~1.5 GB | ~12.0 GB | Marginal — no headroom | Very small |
| q6_K | ~12.1 GB | ~1.5 GB | ~13.6 GB | No | Negligible |
| q8_0 | ~15.7 GB | ~1.5 GB | ~17.2 GB | No | Effectively none |
| fp16 | ~29.5 GB | ~1.5 GB | ~31.0 GB | No | Reference |
The practical answer for both cards is q4_K_M. It leaves roughly 1.5 GB of headroom on a 12 GB card after an 8K context, which absorbs the runtime's compute buffers and the display framebuffer if the card is also driving your monitor. q5_K_M fits arithmetically and fails in practice on a desktop that is also rendering a browser — you will hit an allocation failure or silent layer offload the moment something else claims VRAM.
That conclusion is identical on both cards. This is the single most important sentence in this comparison: the RTX 4070 does not let you run a better quantization than the RTX 3060.
Spec delta: what separates GA106 from AD104?
Every figure below comes from TechPowerUp's database pages for the RTX 3060 12 GB and the RTX 4070, except street prices, which are the SpecPicks catalog snapshot taken 2026-09-22.
| Spec | MSI RTX 3060 12GB | ASUS Dual RTX 4070 12GB | Delta | Why it matters for inference |
|---|---|---|---|---|
| GPU | GA106 (Ampere) | AD104 (Ada Lovelace) | 2 generations | Driver/CUDA maturity vs newer instruction set |
| VRAM | 12 GB GDDR6 | 12 GB GDDR6X | None | Sets the model-size ceiling — identical here |
| Memory bandwidth | 360.0 GB/s | 504.2 GB/s | +40% | Directly sets token-generation speed |
| Bus width | 192-bit | 192-bit | None | Bandwidth gain comes from GDDR6X clocks, not width |
| Shaders | 3,584 | 5,888 | +64% | Drives prompt-processing (prefill) throughput |
| FP32 compute | 12.74 TFLOPS | 29.15 TFLOPS | +129% | Prefill is compute-bound; this is the prefill gap |
| Total graphics power | 170 W | 200 W | +30 W | Both fit a 550-650 W supply comfortably |
| MSRP at launch | $329 | $599 | +82% | Historical reference point |
| Street price (2026-09-22) | ~$479 | ~$749 | +56% | The number your budget actually sees |
| PCIe | 4.0 x16 | 4.0 x16 | None | Irrelevant once the model is resident |
Two rows carry the whole argument. Bandwidth is up 40%; compute is up 129%. Those two numbers predict two different speedups, and which one you feel depends entirely on what you do with the model.
How much faster is the RTX 4070 in tokens per second?
Public figures for local inference vary widely — different runtimes, different quantization builds, different context lengths and different CPU hosts all move the result — so rather than average incompatible numbers into a false precision, the table below shows the bandwidth-derived ceiling and the realistic band beneath it.
The ceiling is straightforward arithmetic. Generating one token with a fully-resident dense model requires reading every weight once, so the theoretical maximum rate is memory bandwidth divided by model size. Real runtimes land at roughly 60-80% of that ceiling because of attention overhead, sampling, and imperfect memory access patterns.
| Model / quant | Card | Bandwidth ÷ weights | Roofline ceiling | Realistic band (60-80%) |
|---|---|---|---|---|
| Qwen2.5 14B q4_K_M (~9.0 GB) | RTX 3060 12GB | 360 ÷ 9.0 | ~40 tok/s | ~24-32 tok/s |
| Qwen2.5 14B q4_K_M (~9.0 GB) | RTX 4070 | 504 ÷ 9.0 | ~56 tok/s | ~34-45 tok/s |
| Qwen2.5 14B q5_K_M (~10.5 GB) | RTX 3060 12GB | 360 ÷ 10.5 | ~34 tok/s | ~21-27 tok/s |
| Qwen2.5 14B q5_K_M (~10.5 GB) | RTX 4070 | 504 ÷ 10.5 | ~48 tok/s | ~29-38 tok/s |
The bandwidth figures are TechPowerUp's; the weight sizes are the Ollama library builds. The ratio between the two cards is fixed at 1.4x regardless of quantization, because both the numerator difference and the denominator are constant.
Prompt processing is a different calculation entirely. Prefill processes the entire input in parallel and saturates the arithmetic units rather than the memory bus, so it scales with the +129% FP32 figure rather than the +40% bandwidth figure. In practice the prefill gap lands closer to 2x than to 1.4x.
Why does prefill scale differently from generation?
Because they are different computations wearing the same name.
Prefill (prompt processing) runs your entire input prompt through the model at once. Every token in the prompt is processed in parallel, which turns the workload into large dense matrix multiplications — exactly the shape GPUs are built for. The weights are read once and reused across thousands of tokens, so memory bandwidth stops being the constraint and raw arithmetic throughput takes over. This is where the RTX 4070's 29.15 TFLOPS against the RTX 3060's 12.74 TFLOPS shows up.
Generation (decode) produces one token at a time, and each token requires reading the entire weight set from VRAM. There is no batching to amortize that read across multiple tokens. A single-user chat session is therefore almost purely bandwidth-bound, and the +40% figure is the one you feel.
The practical consequence: if you paste a 6,000-token document and ask for a summary, the RTX 4070 finishes the "thinking" pause roughly twice as fast, then streams the answer roughly 1.4x as fast. If you type a one-line chat message, prefill is milliseconds on either card and you only experience the 1.4x. Prompt-heavy work is where the money goes.
What happens to throughput as context grows?
The KV cache grows linearly with context, and on a 12 GB card it eventually collides with the weights. Using the 0.19 GB-per-1,000-tokens figure derived above for Qwen2.5 14B at 16-bit KV precision:
| Context | KV cache (fp16) | + q4_K_M weights | Headroom on 12 GB | Practical state |
|---|---|---|---|---|
| 4K | ~0.8 GB | ~9.8 GB | ~2.2 GB | Comfortable on both cards |
| 8K | ~1.5 GB | ~10.5 GB | ~1.5 GB | Fine on a headless box |
| 16K | ~3.0 GB | ~12.0 GB | ~0.0 GB | On the edge — expect offload |
| 32K | ~6.0 GB | ~15.0 GB | Negative | Will not fit; layers spill to system RAM |
Once layers spill to system RAM, throughput collapses on both cards — PCIe 4.0 x16 moves roughly 32 GB/s, an order of magnitude below either card's onboard bandwidth, and the slowest link sets the pace. A partially-offloaded RTX 4070 is slower than a fully-resident RTX 3060. That is the single most important operational fact in this comparison, and it is another place where spending more on the 4070 buys you nothing: both cards hit the offload cliff at the same context length.
Quantizing the KV cache is the standard escape. llama.cpp supports 8-bit KV cache, which halves the table above and pushes the 32K row down to roughly 3 GB, bringing it back inside a 12 GB budget at q4_K_M. Quality impact on the cache is generally far smaller than an equivalent reduction in weight precision, which makes it the first knob to reach for when you need long context on a 12 GB card.
What about the cheaper cards?
ZOTAC Gaming GeForce RTX 3060 Twin Edge OC 12GB is the same GA106 silicon with the same 12 GB of GDDR6 and the same 360 GB/s of bandwidth as the MSI card. For inference the two are interchangeable — the differences are cooler design, acoustics and factory clocks, and factory clocks barely matter when the workload is bandwidth-bound. If the ZOTAC is cheaper on the day you buy, buy the ZOTAC. The catalog lists it at roughly $499 as of 2026-09-22, so on that snapshot the MSI card is actually the better deal, but this is a pairing where you should simply take whichever is lower.
EVGA GeForce GTX 1050 Ti FTW 4GB cannot run this model at any usable quality. Four gigabytes does not hold Qwen2.5 14B at even q2_K, which needs roughly 5.8 GB before any context. You would be running the majority of layers from system RAM over PCIe, which produces token rates measured in low single digits. The card is also Pascal-generation, which lacks the tensor cores modern runtimes assume. It is listed here to close the question, not to recommend it — if 14B is your target, 4 GB is not a budget option, it is a non-option.
Performance per dollar and per watt
Using the realistic midpoints from the roofline table (28 tok/s for the RTX 3060, 40 tok/s for the RTX 4070 at q4_K_M) and the 2026-09-22 catalog prices:
| Metric | RTX 3060 12GB | RTX 4070 | Winner |
|---|---|---|---|
| Street price | ~$479 | ~$749 | RTX 3060 |
| Generation rate (q4_K_M midpoint) | ~28 tok/s | ~40 tok/s | RTX 4070 |
| Tokens/sec per dollar | 28 ÷ 479 = 0.058 | 40 ÷ 749 = 0.053 | RTX 3060, by ~9% |
| Total graphics power | 170 W | 200 W | RTX 3060 |
| Tokens/sec per watt | 28 ÷ 170 = 0.165 | 40 ÷ 200 = 0.200 | RTX 4070, by ~21% |
The two efficiency metrics disagree, and that is the honest result. The RTX 3060 wins on capital efficiency; the RTX 4070 wins on running efficiency because it finishes the same work sooner. For a box that runs a few hours a day, the purchase price dominates and the RTX 3060 is ahead. For a box under sustained load, the watt figure starts to matter and the gap narrows.
Note how close the per-dollar numbers are — 0.058 against 0.053. On this metric these cards are nearly equivalent, which is what you would expect from a market that prices bandwidth rationally. You are not being overcharged for the RTX 4070; you are simply buying more of the same thing.
Common pitfalls on a 12 GB inference build
- Letting the display share the card. A desktop environment with a browser open can hold 500 MB to 1.5 GB of VRAM. That is exactly the headroom q4_K_M leaves at 8K context. Run the box headless, or use integrated graphics for display and leave the discrete card entirely free.
- Picking q5_K_M because it "fits." It fits on paper. It does not fit alongside a compute buffer and a framebuffer. Use q4_K_M and spend the headroom on context.
- Ignoring silent layer offload. Most runtimes will quietly move layers to system RAM rather than fail, so a misconfigured setup looks like it is working while running at a fraction of its potential speed. Check the runtime's startup log for the layer count actually placed on the GPU.
- Assuming PCIe generation matters. It does not, once the model is resident. It matters enormously the moment you are offloading — which is an argument for avoiding offload, not for buying a newer slot standard.
- Buying the 4070 to run a bigger model. It will not. This is the most common and most expensive mistake in this comparison.
When NOT to buy either card
If your roadmap includes 27B-to-32B-class models, buy neither. No amount of bandwidth fixes a capacity ceiling, and both of these cards have the same one. A 16 GB or 24 GB card — including used prior-generation options — removes the offload cliff entirely, and spending once on capacity beats spending twice on 12 GB. The same logic applies if you want 32K-plus context without quantizing the KV cache, or if you intend to serve more than one concurrent request.
Which card should you buy?
Get the RTX 3060 12GB if your work is conversational, your prompts are short, you are price-sensitive, or this is your first local-LLM box and you are still discovering what you actually need. It runs the same models at roughly 70% of the speed for roughly 64% of the price, and it wins on tokens per dollar.
Get the RTX 4070 if your prompts are long — RAG pipelines, document summarization, agent traces with large tool outputs — because that is where the 2x prefill advantage lives rather than the 1.4x generation advantage. Also take it if the machine runs under sustained load, where its better tokens-per-watt figure compounds, or if you also generate images, where Ada's lead over Ampere widens further.
Buy neither if you expect to move past 14B parameters within a year. Put the money toward 16 GB or 24 GB instead.
The recommended pick for most readers is the MSI Gaming GeForce RTX 3060 12GB. The decisive fact is that it loads exactly the same q4_K_M build of Qwen2.5 14B as the ASUS Dual GeForce RTX 4070 OC Edition 12GB, hits the offload cliff at exactly the same context length, and asks roughly $270 less for the privilege. Unless your workload is genuinely prefill-heavy, that $270 is better spent on a 32 GB RAM kit and a larger SSD for your model library — or banked toward the 24 GB card that will actually change what you can run.
Bottom line
Same gate, different throttle. The RTX 3060 12GB and the RTX 4070 run identical quantizations of Qwen2.5 14B and fail at identical context lengths, because 12 GB is 12 GB. The 4070 delivers roughly 1.4x on token generation and roughly 2x on prompt processing for roughly 1.6x the price — a fair trade if you are prefill-bound, a poor one if you are not. Neither card is an upgrade path; both are the same ceiling at different speeds. As of September 2026, buy the cheaper one unless your prompts are long.
Live price comparison
Prices on both cards move week to week. The canonical head-to-head page carries live side-by-side Amazon pricing: RTX 3060 12GB vs RTX 4070 — live comparison.
Prices quoted in this article are a catalog snapshot from 2026-09-22 and may vary.
Related guides
- Best 12GB GPU for Local LLMs in 2026
- Which LLMs Fit on an RTX 3060 12GB in 2026
- Ollama vs llama.cpp on the RTX 3060 12GB
- RTX 3060 12 GB benchmark data
- RTX 4070 benchmark data
Citations and sources
- TechPowerUp — GeForce RTX 3060 12 GB specifications — accessed 2026-09-22
- TechPowerUp — GeForce RTX 4070 specifications — accessed 2026-09-22
- Hugging Face — Qwen2.5-14B-Instruct model card — accessed 2026-09-22
- Ollama library — Qwen2.5 quantization builds and file sizes — accessed 2026-09-22
- llama.cpp — quantization formats and KV-cache options — accessed 2026-09-22
This piece is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported. Throughput figures are derived from published bandwidth and model-size specifications using the roofline method described in the text, and are presented as ranges rather than measurements.
