Introduction

When I run a 40B-class IQuest Coder model inside aider, the first thing I notice is not prompt ingestion. It is the long pause during generation. This memo captured that split very clearly: prefill was fast enough, but decode dropped to 0.6-8 tok/s, which is slow enough to break the rhythm of iterative editing.

A related note from the same period showed IQuest-Coder-V1-40B-Instruct-nvfp4 reaching 25-28 tok/s. That comparison matters. This is not a simple ranking of model families. It is a record of why one serving pattern feels unusable in an edit loop while another feels practical.

Background and Motivation

In coding assistants like aider, the winning trait is not just intelligence. What matters is whether the model can return short, useful updates quickly and repeatedly. If each response is long, slow, and expensive to decode, the user experience degrades even when the model itself is capable.

That is why the core sentence in the source note is so useful:

Prefill is fast enough, but decode is critically slow.

Once that is true, the investigation changes direction. The main question is no longer whether GPU memory or KV cache is insufficient. The real question becomes: why is the generation phase so heavy for this workflow?

Current Evaluation

The source note is direct about the current state:

  • Prefill runs at hundreds to 900 tok/s
  • Decode falls to 0.6-8 tok/s
  • The problem is the generation phase itself, not GPU or KV cache shortage
  • In real aider usage, especially repeated edit loops, the system feels too slow

That split is important. If prefill is already healthy, then the perceived slowness is not about getting the prompt into the model. It is about how long the model spends deciding and emitting tokens once generation starts.

The embedded vLLM logs support that reading.

  (APIServer pid=1) INFO 01-09 19:15:06 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 8.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.2%, Prefix cache hit rate: 6.3%
(APIServer pid=1) INFO 01-09 19:15:16 [loggers.py:257] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 8.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 12.5%, Prefix cache hit rate: 6.3%
(APIServer pid=1) INFO 01-09 19:16:16 [loggers.py:257] Engine 000: Avg prompt throughput: 251.4 tokens/s, Avg generation throughput: 4.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 7.0%, Prefix cache hit rate: 6.4%
(APIServer pid=1) INFO 01-09 19:17:06 [loggers.py:257] Engine 000: Avg prompt throughput: 73.4 tokens/s, Avg generation throughput: 1.1 tokens/s, Running: 0 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.0%, Prefix cache hit rate: 6.8%

KV cache usage sits around 7.0%-13.8%, which does not look like a starved cache regime. Prefix cache hit rate is only 6.3%-6.8%, but those numbers alone do not explain the severity of the slowdown. Decode remains the center of gravity.

Main Causes

The note lists four main causes, and all four line up with how aider tends to stress models.

1. A 40B model plus whole edit produces long outputs

whole edit is convenient for readability, but it also encourages the model to regenerate large chunks of code. Even a small change can turn into a long response, and that is expensive when decode speed is already in the single digits.

The note includes a Django Comment model sample generated during the evaluation.

  from django.db import models
from django.utils.translation import gettext_lazy as _
from core.models import TimeStampedModel, SoftDeleteModel


class Comment(TimeStampedModel, SoftDeleteModel):
    class Status(models.TextChoices):
        PENDING = 'pending', _('Pending')
        APPROVED = 'approved', _('Approved')
        SPAM = 'spam', _('Spam')
        TRASH = 'trash', _('Trash')

    site = models.ForeignKey('core.Site', on_delete=models.CASCADE)
    post = models.ForeignKey('content.Post', on_delete=models.CASCADE)
    parent = models.ForeignKey('self', on_delete=models.CASCADE, null=True, blank=True)
    author_user = models.ForeignKey('auth.User', on_delete=models.SET_NULL, null=True, blank=True)
    author_name = models.CharField(max_length=255)
    author_email = models.EmailField()
    author_url = models.URLField(blank=True)
    body = models.TextField()
    status = models.CharField(max_length=20, choices=Status.choices, default=Status.PENDING)
    ip_hash = models.CharField(max_length=64)
    user_agent = models.TextField(blank=True)

    class Meta:
        indexes = [
            models.Index(fields=['post', 'status', 'created_at']),
        ]

    def __str__(self):
        return f"Comment by {self.author_name} on {self.post}"

    def approve(self):
        self.status = self.Status.APPROVED
        self.save(update_fields=['status'])

    def mark_as_spam(self):
        self.status = self.Status.SPAM
        self.save(update_fields=['status'])

    def move_to_trash(self):
        self.status = self.Status.TRASH
        self.save(update_fields=['status'])

    def restore(self):
        self.status = self.Status.PENDING
        self.save(update_fields=['status'])

    def is_approved(self):
        return self.status == self.Status.APPROVED

The model can clearly produce substantial code. The problem is that this response style becomes expensive when it is repeated inside an edit loop.

2. The context is too large

The note explicitly calls out repo-map and multiple added files. That kind of context expansion can improve relevance, but it also makes each request heavier and tends to encourage longer replies. In practice, that means decode gets punished twice: the model sees more context and often answers with more output.

3. Sampling makes decode heavier

One of the recommended fixes is to set temperature=0 and use greedy decoding. That makes sense. For code editing, determinism and quick termination often matter more than stylistic variation.

4. A 40B-class model may simply be a poor fit for repeated single-request decode

This point becomes clearer when I compare it with the related nvfp4 note. That note recorded:

  • Prompt throughput: 1100-2300 tok/s
  • Generation throughput: 25-28 tok/s
  • KV cache usage: 2-12%
  • Prefix cache hit rate: 20-45%

So the lesson is not that “40B is inherently unusable.” The lesson is that a 40B model plus whole edit, bloated context, and non-greedy generation can land in a very bad operating point for aider.

Immediate Countermeasures

The source note already contains the right short-term actions:

  1. Replace whole edit with diff/patch
  2. Use temperature=0
  3. Reduce repo-map size and /add scope
  4. Keep max_model_len to the minimum required
  5. If needed, move to a quantized or lighter coding model in the 20B-32B range

The order matters. Because prefill is already acceptable, the best fixes are the ones that reduce decode volume and decode branching first.

Conclusion

The source note closes with a practical conclusion, and I agree with it:

  • This throughput is not “good enough because it uses a GPU”; it is still slow for real work
  • For aider, productivity depends more on short, fast outputs than on maximum model size
  • Configuration can improve things, but 40B + whole edit is fundamentally a poor match here

What this benchmark made clear is that prefill speed does not decide the real user experience. In iterative coding workflows, decode latency dominates. If I want a model to feel good in aider, I need to optimize for short outputs, small context, and predictable termination before I optimize for raw model scale.