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⇱ RTX 4070 SUPER Local LLM Benchmarks, Context Scaling & Supported Models 2026 – Hardware Corner

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RTX 4070 SUPER LLM Performance

Local LLM Performance: 37.2 t/s average on 14B models at 16k context. Updated Benchmarks: March 2026.

Gen (14B 4-bit) 37.2 t/s
PP (14B 4-bit) 1,578 t/s
Max Model 14B
VRAM
12 GB GDDR6X
Bandwidth 504 GB/s
Token Gen (14B @ 4k Ctx)

37.2T/s

Prompt Proc (14B @ 4k Ctx)

1,578T/s

Summary

In our 2026 benchmarking, we found the RTX 4070 SUPER to be a fast but strategically awkward card for local AI. While it slightly outpacing the RTX 5060 Ti 16GB in prompt processing, its 12GB VRAM limitation is a significant bottleneck. At current price, we do not believe it makes much sense for this specific use case; the price-per-GB is poor, and most users would be better served by opting for 16GB alternatives that offer similar generation speeds with much higher model capacity.

Key Insights

Capable of running Qwen3 14B using GGUF (Q4_K) completely in VRAM.
Delivers slightly faster prompt processing (1,578 t/s) than the RTX 5060 Ti 16GB.
Matches the RTX 5060 Ti 16GB in token generation speed at roughly 37 t/s.
Restricted to 14B models and limited context due to the 12GB VRAM capacity.

Current Price in US

$490

Avg. Market Value

Current Pricing

Hardware Specs
VRAM 12GB GDDR6X
Capable of running 14B model
Bandwidth 504 GB/s
Architecture Ada Lovelace
Memory speed 21 Gbps
Memory bus 192 bit
TDP 220 W
Suggested PSU 650 W
Price/GB VRAM $40.83
Price/(t/s) with 14B @ 16k $13.19

Biggest LLMs You Can Run on This GPU

The models below represent the largest language models that fit fully in VRAM on this GPU using 4-bit quantization (GGUF). Benchmarks include token generation and prompt processing speeds measured at their maximum supported context length.

Qwen3 14B (Q4_K) Max 16k
Token Generation 37.2 t/s @ 16k context
Prompt Processing 1,578.1 t/s @ 16k context
Qwen3 8B (Q4_K) Max 32k
Token Generation 42.2 t/s @ 32k context
Prompt Processing 1,595.7 t/s @ 32k context

Note: Context values are grouped into standard tiers (4K, 16K, 32K, 64K, 128K). Models may support slightly higher context, but they remain in the lower tier unless they reach the next bracket.

RTX 4070 SUPER local LLM Inference Performance vs Similar GPUs

Compare prompt ingestion and token generation speeds against similar GPUs across widely used local models and extended context lengths up to 256K.

Local LLM Benchmarks

Prompt processing (t/s) and token generation speed (t/s) across different open weight models and context lengths.

Prompt Processing
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 4,321.7 2,525.8 1,595.7 — — —
Qwen3 14B (Q4_K) 2,522.2 1,578.1 — — — —
Token Generation
Model 4k Ctx 16k Ctx 32k Ctx 64k Ctx 128k Ctx 256k Ctx
Qwen3 8B (Q4_K) 75.4 56.2 42.2 — — —
Qwen3 14B (Q4_K) 45.5 37.2 — — — —

Frequently Asked Questions

Common questions about running LLMs on the RTX 4070 SUPER.